{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}E:\Dropbox\acad_afalco\CoronaSUTVA\PSRM Submission\Replication\Replication_log.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}20 May 2025, 12:06:05
{txt}
{com}. 
. ***************************************************************************************************************************************
. *This is the code to replicate the results of the article "Pandemics meet democracy: The footprint of COVID-19 on democratic attitudes"
. *Read the README.txt file first
. *March 2025
. ***************************************************************************************************************************************
. 
. clear all
{res}{txt}
{com}. 
. 
. ***************************
. *Install required packages*
. ***************************
. 
. ssc install reghdfe, replace
{txt}checking {hilite:reghdfe} consistency and verifying not already installed...
all files already exist and are up to date.

{com}. ssc install ftools, replace
{txt}checking {hilite:ftools} consistency and verifying not already installed...
all files already exist and are up to date.

{com}. ssc install combomarginsplot, replace
{txt}checking {hilite:combomarginsplot} consistency and verifying not already installed...
all files already exist and are up to date.

{com}. ssc install mplotoffset, replace
{txt}checking {hilite:mplotoffset} consistency and verifying not already installed...
all files already exist and are up to date.

{com}. ssc install binscatter, replace
{txt}checking {hilite:binscatter} consistency and verifying not already installed...
all files already exist and are up to date.

{com}. ssc install coefplot, replace
{txt}checking {hilite:coefplot} consistency and verifying not already installed...
all files already exist and are up to date.

{com}. ssc install schemepack, replace
{txt}checking {hilite:schemepack} consistency and verifying not already installed...
all files already exist and are up to date.

{com}. 
. 
. *************************
. *Set scheme for graphics*
. *************************
. 
. set scheme white_tableau
{txt}
{com}. 
. 
. 
. *****************************
. use pandemics_data.dta, clear
{txt}
{com}. *****************************
. 
. 
. **********************************************************************************************************
. *************************************************MAIN TEXT************************************************
. **********************************************************************************************************
. 
. 
. ***************************************************************************************************
. *Figure 1: Effect of the outbreak of COVID-19 on technocratic attitudes (individual fixed effects)*
. ***************************************************************************************************
. 
. quietly {c -(}
{txt}
{com}. 
. combomarginsplot tec_performancevoting tec_techmanagement tec_technocrats, labels("Vote based on management, not ideas" "Technical management, not ideological" "Experts, not politicians") xline(721.5, lc(gray)) title("Technocratic preferences", size(*.8) margin(b=2)) xtitle("") ytitle("Degree of agreement (1-7 scale)") yscale(titlegap(*0)) ylabel(4(.5)6, labsize(*.8) nogrid) xlabel(720 `" "Jan" "20" "' 722 `" "Mar" "20" "' 725 `" "Jun" "20" "' 730 `" "Nov" "20" "' 734 `" "Mar" "21" "' 740 `" "Sep" "21" "' 754 `" "Nov" "22" "' 768 `" "Jan" "24" "', labsize(*.8) nogrid angle(horizontal)) xscale(range(719 769)) legend(pos(0) bplacement(seast) c(1) size(small) region(fcol(none))) offset(.3) saving(tec_combo,replace)
{res}
{text}{p 2 6 2}Variables that uniquely identify margins: datewave _filenumber{p_end}
{res}{txt}{p 0 4 2}
(file {bf}
tec_combo.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:tec_combo.gph} saved

{com}. graph export figure_1.pdf, replace
{txt}{p 0 4 2}
file {bf}
figure_1.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. 
. 
. ***************************************************************************************************************
. *Figure 2: Effect of the outbreak of COVID-19 on preferred qualities of politicians (individual fixed effects)*
. ***************************************************************************************************************
. 
. quietly {c -(}
{txt}
{com}. 
. combomarginsplot tec_mic_honesty tec_mic_capacity tec_mic_ideology, labels("Honesty" "Capacity" "Ideology") xline(721.5, lc(gray)) title("Preferences for politicians' qualities", size(*.8) margin(b=2)) xtitle("") ytitle("Likelihood of quality ranked first") yscale(titlegap(*0) range(0 .6)) ylabel(0(.1).6, labsize(*.8) nogrid) xlabel(720 `" "Jan" "20" "' 722 `" "Mar" "20" "' 725 `" "Jun" "20" "' 730 `" "Nov" "20" "' 734 `" "Mar" "21" "' 740 `" "Sep" "21" "' 754 `" "Nov" "22" "' 768 `" "Jan" "24" "', labsize(*.8) nogrid angle(horizontal)) xscale(range(719 769)) legend(pos(6) r(1) size(small) region(fcol(none))) offset(0) saving(tec_mic_combo, replace)
{res}
{text}{p 2 6 2}Variables that uniquely identify margins: datewave _filenumber{p_end}
{res}{txt}{p 0 4 2}
(file {bf}
tec_mic_combo.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:tec_mic_combo.gph} saved

{com}. graph export figure_2.pdf, replace
{txt}{p 0 4 2}
file {bf}
figure_2.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. 
. 
. *********************************************************************************************************************************
. *Figure 3: Effect of different threats on willingness to sacrifice liberties and need of a strong leader (experimental evidence)*
. *********************************************************************************************************************************
. 
. quietly {c -(}
{txt}
{com}. 
. graph combine threats_lib.gph threats_strong.gph, c(2) imargin(small) ycommon
{res}{txt}
{com}. graph export figure_3.pdf, replace
{txt}{p 0 4 2}
file {bf}
figure_3.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. 
. 
. **********************************************************************************************************
. *************************************************APPENDIX*************************************************
. **********************************************************************************************************
. 
. *************************************************
. *Table A1: Sample descriptive statistics by wave*
. *************************************************
. 
. quietly {c -(}
{txt}
{com}. 
. variable_group_table gender age agecat* educat* incumbent lr lrcat* nobswave, by(nwave) stat(mean) format(%9.3gc)

{txt}{hline 28}{c TT}{hline 55}
                            {c |}                         nwave                         
                            {c |}     1      2      3      4      5      6      7      8
{hline 28}{c +}{hline 55}
                     Gender {c |}  {res}.511   .508   .486   .511   .491   .472   .512   .492
                        {txt}Age {c |}  {res}45.4   45.5     47     46   46.9   48.2   48.6   50.5
             {txt}Age (Under-35) {c |}  {res}.269   .271   .232   .265   .239   .224   .246     .2
                {txt}Age (35-60) {c |}  {res}.537   .529   .549   .521    .54   .517    .45   .471
              {txt}Age (Over-60) {c |}  {res}.194     .2   .219   .215   .222   .259   .304   .328
{txt}Education (Primary or less) {c |}  {res}.435   .435   .398   .422   .397   .367   .434   .386
      {txt}Education (Secondary) {c |}  {res}.263   .264   .284   .274   .276   .242   .262   .283
       {txt}Education (Tertiary) {c |}  {res}.303   .301   .318   .303   .326   .391   .304   .331
 {txt}Incumbent supporter (PSOE) {c |}  {res}.201   .182   .199   .174   .187   .168   .183   .193
                 {txt}Left-right {c |}  {res}4.28   4.37   4.42    4.5    4.5   4.61   4.63   4.57
     {txt}Left-right (Left: 0-4) {c |}  {res}.565   .522   .506   .481   .485   .478   .437   .462
     {txt}Left-right (Center: 5) {c |}  {res}.197   .225   .245    .26   .253   .234   .282   .254
   {txt}Left-right (Right: 6-10) {c |}  {res}.239   .253   .249   .259   .262   .288   .281   .285
                          {txt}N {c |} {res}1,008  1,606  1,349  1,608  1,253    773  1,602  2,198
{txt}{hline 28}{c BT}{hline 55}

{com}. 
. 
. **********************************************************************
. *Table B1: Measurement of technocratic attitudes (correlation matrix)*
. **********************************************************************
. 
. quietly {c -(}
{txt}
{com}. 
. esttab corr*, unstack not compress nonum noobs nodep b(%9.3f) star(* 0.10 ** 0.05 *** 0.01) mlabels("Management-based voting" "Technical management" "Experts not politicians" "Average")
{res}
{txt}{hline 62}
{txt}           Managem~g    Technic~t    Experts~s      Average   
{txt}{hline 62}
{txt}elitism1  {res}    -0.018        0.021        0.063***     0.026*  {txt}
{txt}elitism2  {res}    -0.033**     -0.052***    -0.059***    -0.064***{txt}
{txt}elitism3  {res}     0.005        0.040***     0.027**      0.031** {txt}
{txt}elitism4  {res}     0.051***     0.071***     0.079***     0.090***{txt}
{txt}experts1  {res}     0.154***     0.280***     0.297***     0.324***{txt}
{txt}experts2  {res}     0.113***     0.189***     0.206***     0.226***{txt}
{txt}experts3  {res}     0.196***     0.296***     0.352***     0.376***{txt}
{txt}experts4  {res}     0.158***     0.312***     0.387***     0.378***{txt}
{txt}antipoli~1{res}     0.177***     0.332***     0.468***     0.430***{txt}
{txt}antipoli~2{res}     0.174***     0.267***     0.378***     0.363***{txt}
{txt}antipoli~3{res}     0.099***     0.167***     0.229***     0.219***{txt}
{txt}antipoli~4{res}     0.180***     0.258***     0.321***     0.338***{txt}
{txt}{hline 62}
{txt}* p<0.10, ** p<0.05, *** p<0.01

{com}. 
. 
. ********************************************************
. *Figure C1: Comparison panelists vs rest of respondents*
. ********************************************************
. 
. quietly {c -(}
{txt}
{com}. 
. graph combine tec_firstwave.gph tec_firsttime.gph, c(2) imargin(small) ycommon
{res}{txt}
{com}. graph export figure_c1.pdf, replace
{txt}{p 0 4 2}
file {bf}
figure_c1.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. 
. 
. ******************************************************
. *Figure D1: Technocratic preferences by initial shift*
. *******************************************************
. 
. quietly {c -(}
{txt}
{com}. 
. graph combine a1 a2, rows(1)  name(a1a6, replace) note("Controlling for pre-covid levels: sample split by the residuals of a regression of the initial shift on pre-covid levels") xcommon ycommon
{res}{txt}
{com}. graph export figure_d1.pdf, replace
{txt}{p 0 4 2}
file {bf}
figure_d1.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. 
. 
. ******************************************************************************
. *Figure D2: Immediate change in technocratic attitudes (March - January 2020)*
. ******************************************************************************
. 
. twoway kdensity delta_techatt if wave==2, title("Difference in technocratic attitudes between March and January 2020", size(medsmall)) ///
>         ytitle("Density") xtitle("Difference in attitudes") ylabel(, nogrid) xlabel(, nogrid)
{res}{txt}
{com}. graph export figure_d2.pdf, replace
{txt}{p 0 4 2}
file {bf}
figure_d2.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. 
. 
. *******************************************************************************
. *Figure D3: Immediate change in technocratic attitudes vs. pre-existing levels*
. *******************************************************************************
. 
. binscatter delta_techatt techatt_w1 if wave==2, xtitle("Pre-pandemic Technocratic Preferences") ytitle("Immediate change in Technocratic Preferences") ///
>         ylabel(, nogrid) xlabel(, nogrid)
{res}{txt}warning: nquantiles(20) was specified, but only 12 were generated. see help file under nquantiles() for explanation.
{res}{txt}
{com}. graph export figure_d3.pdf, replace
{txt}{p 0 4 2}
file {bf}
figure_d3.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. 
. 
. ***********
. *Figure D4*
. ***********
. 
. quietly {c -(}
{txt}
{com}. 
. coefplot (a0, label("Baseline")) (a1, label("Controlling for wave-by-January 2020 levels")), ///
>         omitted keep(zero $coefinter)  graphregion(color(white))  vertical  ///
>         title("Technocractic attitudes (avg.), wave-by-initial shift coefficients") ///
>         xlabel(1 "Jan 2020" 2 "Mar 2020" 3 "Jun 2020" 4 "Nov 2020" 5 "Mar 2021" 6 "Sep 2021" 7 "Nov 2022" 8 "Jan 2024" , angle(45) nogrid) xline( 1.5) ylabel(, nogrid) ///
>         legend(pos(6) rows(1))
{res}{txt}(a0: CI1 missing for some coefficients)
{res}{txt}(a1: CI1 missing for some coefficients)
{res}{txt}
{com}. graph export figure_d4.pdf, replace
{txt}{p 0 4 2}
file {bf}
figure_d4.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. 
. 
. ***********
. *Figure D5*
. ***********
. 
. quietly {c -(}
{txt}
{com}. 
. graph combine a1a3a5 a2a4a6, rows(1) title("Technocratic preferences by initial shift", size(*.8) margin(b=2)) name(a1a6, replace) note("Controlling for pre-covid levels: sample split by the residuals of a regression of the initial shift on pre-covid levels") xcommon ycommon
{res}{txt}
{com}. graph export figure_d5.pdf, replace
{txt}{p 0 4 2}
file {bf}
figure_d5.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. 
. 
. ************************************************************************
. *Figure D6: Permutation tests: distribution of placebo pandemic effects*
. ************************************************************************
. 
. quietly {c -(}
{txt}
{com}. 
. graph combine a1 a2 a3 a4, rows(2)
{res}{txt}
{com}. graph export figure_d6.pdf, replace
{txt}{p 0 4 2}
file {bf}
figure_d6.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. 
. 
. **********************************************************************************************************
. *Figure E1: Heterogeneity of effects on technocratic attitudes (panel evidence, individual fixed effects)*
. **********************************************************************************************************
. 
. quietly {c -(}
{txt}
{com}. 
. graph combine tec_gender.gph tec_age.gph tec_edu.gph tec_interest.gph tec_incumbent.gph tec_lr.gph, c(3) imargin(tiny) title("Technocratic preferences (average) - Heterogeneity", size(small))
{res}{txt}
{com}. graph export figure_e1.pdf, replace
{txt}{p 0 4 2}
file {bf}
figure_e1.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. 
. 
. ******************************************************************************************************************
. *Figure E2: Exposure to COVID-related risks and technocratic attitudes (panel evidence, individual fixed effects)*
. ******************************************************************************************************************
. 
. quietly{c -(}
{txt}
{com}. 
. graph combine tec_infected.gph tec_old.gph tec_healthrisk.gph tec_econriskbin.gph tec_predhealthrisk.gph tec_predeconrisk.gph , c(3) imargin(tiny) title("Technocratic preferences (average) - By exposure and risk", size(small))
{res}{txt}
{com}. graph export figure_e2.pdf, replace
{txt}{p 0 4 2}
file {bf}
figure_e2.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. 
. 
. ***************************************************************************************************
. *Figure E3: Heterogeneity of effects on willingness to sacrifice liberties (experimental evidence)*
. ***************************************************************************************************
. 
. quietly {c -(}
{txt}
{com}. 
. graph combine th_lib_gender.gph th_lib_age.gph th_lib_edu.gph th_lib_int.gph th_lib_inc.gph th_lib_lr.gph , c(3) imargin(tiny) title("Willingness to sacrifice liberties - Heterogeneity", size(small))
{res}{txt}
{com}. graph export figure_e3.pdf, replace
{txt}{p 0 4 2}
file {bf}
figure_e3.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. 
. 
. ****************************************************************************************
. *Figure E4: Heterogeneity of effects on need of a strong leader (experimental evidence)*
. ****************************************************************************************
. 
. quietly {c -(}
{txt}
{com}. 
. graph combine th_strong_gender.gph th_strong_age.gph th_strong_edu.gph th_strong_int.gph th_strong_inc.gph th_strong_lr.gph , c(3) imargin(tiny) title("Need of a strong leader - Heterogeneity", size(small))
{res}{txt}
{com}. graph export figure_e4.pdf, replace
{txt}{p 0 4 2}
file {bf}
figure_e4.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. 
. 
. ***********************************************************************************************************
. *Figure E5: Exposure to COVID-related risks and willingness to sacrifice liberties (experimental evidence)*
. ***********************************************************************************************************
. 
. quietly {c -(}
{txt}
{com}. 
. graph combine th_lib_infected.gph th_lib_old.gph th_lib_healthrisk.gph th_lib_econriskbin.gph th_lib_predhealthrisk.gph th_lib_predeconrisk.gph , c(3) imargin(tiny) title("Willingness to sacrifice liberties - By exposure and risk", size(small))
{res}{txt}
{com}. graph export figure_e5.pdf, replace
{txt}{p 0 4 2}
file {bf}
figure_e5.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. 
. 
. ************************************************************************************************
. *Figure E6: Exposure to COVID-related risks and need of a strong leader (experimental evidence)*
. ************************************************************************************************
. 
. quietly {c -(}
{txt}
{com}. 
. graph combine th_strong_infected.gph th_strong_old.gph th_strong_healthrisk.gph th_strong_econriskbin.gph th_strong_predhealthrisk.gph th_strong_predeconrisk.gph , c(3) imargin(tiny) title("Need of a strong leader -  By exposure and risk", size(small))
{res}{txt}
{com}. graph export figure_e6.pdf, replace
{txt}{p 0 4 2}
file {bf}
figure_e6.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. 
. 
. ********************************************************************************************************************************************
. *Table F1: Regression estimates on the effect of outbreak of COVID-19 on technocratic attitudes (full models for Figure 1 in the main text)*
. ********************************************************************************************************************************************
. 
. quietly {c -(}
{txt}
{com}. 
. esttab tec_performancevoting_tab tec_techmanagement_tab tec_technocrats_tab tec_average_tab , replace nonumbers nogaps noomit nobase label noobs nonotes ///
>   order(_cons) coeflabels(_cons "Intercept (January 2020)" 722.datewave "March 2020" 725.datewave "June 2020" 730.datewave "November 2020" 734.datewave "March 2021" 740.datewave "September 2021" 754.datewave "November 2022" 768.datewave "January 2024") ///
>   b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
>   collabels("") scalars("ife Individual FE" "n N. of observations" "unique N. of unique respondents" "r2 R$^2$") ///
>   mlabels("Performance" "Management" "Rulers" "Average") ///
>   addnotes("Standard errors clustered by individual in parentheses. *p$<$.1; **p$<$.05; ***p$<$.01.")
{res}
{txt}{hline 84}
{txt}                      Performance      Management          Rulers         Average   
{txt}                                                                                    
{txt}{hline 84}
{txt}Intercept (Jan~2020){res}        4.584***        4.990***        4.734***        4.782***{txt}
                    {res} {ralign 12:{txt:(}0.047{txt:)}}    {ralign 12:{txt:(}0.044{txt:)}}    {ralign 12:{txt:(}0.035{txt:)}}    {ralign 12:{txt:(}0.030{txt:)}}   {txt}
{txt}March 2020          {res}        0.449***        0.225***        0.293***        0.307***{txt}
                    {res} {ralign 12:{txt:(}0.056{txt:)}}    {ralign 12:{txt:(}0.057{txt:)}}    {ralign 12:{txt:(}0.044{txt:)}}    {ralign 12:{txt:(}0.037{txt:)}}   {txt}
{txt}June 2020           {res}        0.346***        0.268***        0.263***        0.276***{txt}
                    {res} {ralign 12:{txt:(}0.061{txt:)}}    {ralign 12:{txt:(}0.059{txt:)}}    {ralign 12:{txt:(}0.047{txt:)}}    {ralign 12:{txt:(}0.038{txt:)}}   {txt}
{txt}November 2020       {res}        0.431***        0.317***        0.334***        0.348***{txt}
                    {res} {ralign 12:{txt:(}0.061{txt:)}}    {ralign 12:{txt:(}0.057{txt:)}}    {ralign 12:{txt:(}0.048{txt:)}}    {ralign 12:{txt:(}0.039{txt:)}}   {txt}
{txt}March 2021          {res}        0.505***        0.213***        0.280***        0.306***{txt}
                    {res} {ralign 12:{txt:(}0.061{txt:)}}    {ralign 12:{txt:(}0.058{txt:)}}    {ralign 12:{txt:(}0.049{txt:)}}    {ralign 12:{txt:(}0.040{txt:)}}   {txt}
{txt}September 2021      {res}        0.465***        0.117*          0.237***        0.241***{txt}
                    {res} {ralign 12:{txt:(}0.070{txt:)}}    {ralign 12:{txt:(}0.063{txt:)}}    {ralign 12:{txt:(}0.051{txt:)}}    {ralign 12:{txt:(}0.043{txt:)}}   {txt}
{txt}November 2022       {res}        0.406***        0.099*          0.173***        0.206***{txt}
                    {res} {ralign 12:{txt:(}0.066{txt:)}}    {ralign 12:{txt:(}0.060{txt:)}}    {ralign 12:{txt:(}0.047{txt:)}}    {ralign 12:{txt:(}0.041{txt:)}}   {txt}
{txt}January 2024        {res}        0.414***        0.067           0.172***        0.205***{txt}
                    {res} {ralign 12:{txt:(}0.065{txt:)}}    {ralign 12:{txt:(}0.057{txt:)}}    {ralign 12:{txt:(}0.046{txt:)}}    {ralign 12:{txt:(}0.039{txt:)}}   {txt}
{txt}{hline 84}
{txt}Individual FE       {res}          Yes             Yes             Yes             Yes   {txt}
{txt}N. of observations  {res}         8936            9445            9427            9663   {txt}
{txt}N. of unique respo~s{res}         2073            2174            2172            2216   {txt}
{txt}R$^2$               {res}        0.601           0.508           0.614           0.630   {txt}
{txt}{hline 84}
{txt}Standard errors clustered by individual in parentheses. *p$<$.1; **p$<$.05; ***p$<$.01.

{com}. 
. 
.   
. ************************************************************************************************************************************************************
. *Table F2: Regression estimates on the effect of outbreak of COVID-19 on preferences for politicians' qualities (full models for Figure 2 in the main text)*
. ************************************************************************************************************************************************************
. 
. quietly {c -(}
{txt}
{com}. 
. esttab tec_mic_honesty_tab tec_mic_capacity_tab tec_mic_ideology_tab tec_mic_training_tab tec_mic_approachable_tab, replace nonumbers nogaps noomit nobase label noobs nonotes ///
>   order(_cons) coeflabels(_cons "Intercept (January 2020)" 722.datewave "March 2020" 725.datewave "June 2020" 730.datewave "November 2020" 734.datewave "March 2021" 740.datewave "September 2021" 754.datewave "November 2022" 768.datewave "January 2024") ///
>   b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
>   collabels("") scalars("ife Individual FE" "n N. of observations" "unique N. of unique respondents" "r2 R$^2$") ///
>   mlabels("Honesty" "Capacity" "Ideology" "Training" "Approachable") ///
>   addnotes("Standard errors clustered by individual in parentheses. *p$<$.1; **p$<$.05; ***p$<$.01.")
{res}
{txt}{hline 100}
{txt}                          Honesty        Capacity        Ideology        Training    Approachable   
{txt}                                                                                                    
{txt}{hline 100}
{txt}Intercept (Jan~2020){res}        0.548***        0.145***        0.032***        0.218***        0.057***{txt}
                    {res} {ralign 12:{txt:(}0.015{txt:)}}    {ralign 12:{txt:(}0.012{txt:)}}    {ralign 12:{txt:(}0.006{txt:)}}    {ralign 12:{txt:(}0.013{txt:)}}    {ralign 12:{txt:(}0.007{txt:)}}   {txt}
{txt}March 2020          {res}       -0.102***        0.074***       -0.004           0.049***       -0.018** {txt}
                    {res} {ralign 12:{txt:(}0.019{txt:)}}    {ralign 12:{txt:(}0.015{txt:)}}    {ralign 12:{txt:(}0.007{txt:)}}    {ralign 12:{txt:(}0.016{txt:)}}    {ralign 12:{txt:(}0.008{txt:)}}   {txt}
{txt}June 2020           {res}       -0.113***        0.078***       -0.004           0.050***       -0.011   {txt}
                    {res} {ralign 12:{txt:(}0.019{txt:)}}    {ralign 12:{txt:(}0.016{txt:)}}    {ralign 12:{txt:(}0.007{txt:)}}    {ralign 12:{txt:(}0.017{txt:)}}    {ralign 12:{txt:(}0.009{txt:)}}   {txt}
{txt}November 2020       {res}       -0.091***        0.082***        0.004           0.026          -0.021** {txt}
                    {res} {ralign 12:{txt:(}0.020{txt:)}}    {ralign 12:{txt:(}0.016{txt:)}}    {ralign 12:{txt:(}0.008{txt:)}}    {ralign 12:{txt:(}0.017{txt:)}}    {ralign 12:{txt:(}0.009{txt:)}}   {txt}
{txt}March 2021          {res}       -0.102***        0.092***        0.009           0.019          -0.019** {txt}
                    {res} {ralign 12:{txt:(}0.021{txt:)}}    {ralign 12:{txt:(}0.017{txt:)}}    {ralign 12:{txt:(}0.008{txt:)}}    {ralign 12:{txt:(}0.018{txt:)}}    {ralign 12:{txt:(}0.009{txt:)}}   {txt}
{txt}September 2021      {res}       -0.089***        0.082***        0.004           0.016          -0.013   {txt}
                    {res} {ralign 12:{txt:(}0.023{txt:)}}    {ralign 12:{txt:(}0.018{txt:)}}    {ralign 12:{txt:(}0.009{txt:)}}    {ralign 12:{txt:(}0.019{txt:)}}    {ralign 12:{txt:(}0.010{txt:)}}   {txt}
{txt}November 2022       {res}       -0.078***        0.068***       -0.006           0.037**        -0.021** {txt}
                    {res} {ralign 12:{txt:(}0.021{txt:)}}    {ralign 12:{txt:(}0.017{txt:)}}    {ralign 12:{txt:(}0.008{txt:)}}    {ralign 12:{txt:(}0.018{txt:)}}    {ralign 12:{txt:(}0.009{txt:)}}   {txt}
{txt}January 2024        {res}       -0.064***        0.062***        0.003           0.013          -0.014   {txt}
                    {res} {ralign 12:{txt:(}0.021{txt:)}}    {ralign 12:{txt:(}0.017{txt:)}}    {ralign 12:{txt:(}0.008{txt:)}}    {ralign 12:{txt:(}0.017{txt:)}}    {ralign 12:{txt:(}0.009{txt:)}}   {txt}
{txt}{hline 100}
{txt}Individual FE       {res}          Yes             Yes             Yes             Yes             Yes   {txt}
{txt}N. of observations  {res}        10032           10032           10032           10032           10032   {txt}
{txt}N. of unique respo~s{res}         2287            2287            2287            2287            2287   {txt}
{txt}R$^2$               {res}        0.444           0.395           0.361           0.407           0.363   {txt}
{txt}{hline 100}
{txt}Standard errors clustered by individual in parentheses. *p$<$.1; **p$<$.05; ***p$<$.01.

{com}. 
.   
. 
. *********************************************************************************************************************************************************************************
. *Table F3: Regression estimates of effect of different threats on willingness to sacrifice liberties and support for a strong leader (full models for Figure 3 in the main text)*
. *********************************************************************************************************************************************************************************
. 
. quietly {c -(}
{txt}
{com}. 
. esttab threats_lib_tab threats_lib_fe_tab threats_strong_tab threats_strong_fe_tab, replace nonumbers nogaps noomit label noobs nomtitles nonotes ///
>   order(_cons) coeflabels(_cons "Intercept" 1.threat "Threat (Covid)" 2.threat "Threat (Climate change)" 3.threat "Threat (International terrorism)" 722.datewave "March 2020" 725.datewave "June 2020" 730.datewave "November 2020" 734.datewave "March 2021" 740.datewave "September 2021" 754.datewave "November 2022" 768.datewave "January 2024" 2.threat#725.datewave "Threat (Climate) $\times$ June 2020" 2.threat#730.datewave "Threat (Climate) $\times$ November 2020" 2.threat#734.datewave "Threat (Climate) $\times$ March 2021" 2.threat#740.datewave "Threat (Climate) $\times$ September 2021" 2.threat#754.datewave "Threat (Climate) $\times$ November 2022" 2.threat#768.datewave "Threat (Climate) $\times$ January 2024" 3.threat#725.datewave "Threat (Terrorism) $\times$ June 2020" 3.threat#730.datewave "Threat (Terrorism) $\times$ November 2020" 3.threat#734.datewave "Threat (Terrorism) $\times$ March 2021" 3.threat#740.datewave "Threat (Terrorism) $\times$ September 2021" 3.threat#754.datewave "Threat (Climate) $\times$ November 2022" 3.threat#768.datewave "Threat (Climate) $\times$ January 2024") ///
>   b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
>   collabels("") scalars("ife Individual FE" "n N. of observations" "unique N. of unique respondents" "r2 R$^2$") ///
>   mgroups("Sacrifice liberties" "Strong leader", pattern(1 0 1 0) span) /// ///
>   addnotes("Standard errors clustered by individual in parentheses. *p$<$.1; **p$<$.05; ***p$<$.01.")
{res}
{txt}{hline 84}
{txt}                     Sacrifice liberties             Strong leader                  
{txt}                                                                                    
{txt}{hline 84}
{txt}Intercept           {res}        8.215***        8.328***        7.841***        7.808***{txt}
                    {res} {ralign 12:{txt:(}0.105{txt:)}}    {ralign 12:{txt:(}0.128{txt:)}}    {ralign 12:{txt:(}0.107{txt:)}}    {ralign 12:{txt:(}0.099{txt:)}}   {txt}
{txt}Threat (Covid)      {res}        0.000           0.000           0.000           0.000   {txt}
                    {res} {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}   {txt}
{txt}Threat (Climate ch~){res}       -2.083***       -2.280***       -0.595***       -0.534***{txt}
                    {res} {ralign 12:{txt:(}0.160{txt:)}}    {ralign 12:{txt:(}0.188{txt:)}}    {ralign 12:{txt:(}0.148{txt:)}}    {ralign 12:{txt:(}0.149{txt:)}}   {txt}
{txt}Threat (Internatio~){res}       -2.706***       -2.682***       -0.744***       -0.675***{txt}
                    {res} {ralign 12:{txt:(}0.162{txt:)}}    {ralign 12:{txt:(}0.188{txt:)}}    {ralign 12:{txt:(}0.149{txt:)}}    {ralign 12:{txt:(}0.150{txt:)}}   {txt}
{txt}March 2020          {res}        0.000           0.000           0.000           0.000   {txt}
                    {res} {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}   {txt}
{txt}June 2020           {res}       -1.831***       -1.852***       -0.572***       -0.401***{txt}
                    {res} {ralign 12:{txt:(}0.164{txt:)}}    {ralign 12:{txt:(}0.177{txt:)}}    {ralign 12:{txt:(}0.148{txt:)}}    {ralign 12:{txt:(}0.142{txt:)}}   {txt}
{txt}November 2020       {res}       -1.738***       -1.734***       -0.664***       -0.552***{txt}
                    {res} {ralign 12:{txt:(}0.154{txt:)}}    {ralign 12:{txt:(}0.166{txt:)}}    {ralign 12:{txt:(}0.142{txt:)}}    {ralign 12:{txt:(}0.137{txt:)}}   {txt}
{txt}March 2021          {res}       -2.153***       -2.243***       -1.237***       -1.128***{txt}
                    {res} {ralign 12:{txt:(}0.175{txt:)}}    {ralign 12:{txt:(}0.186{txt:)}}    {ralign 12:{txt:(}0.162{txt:)}}    {ralign 12:{txt:(}0.152{txt:)}}   {txt}
{txt}September 2021      {res}       -2.742***       -2.888***       -1.558***       -1.518***{txt}
                    {res} {ralign 12:{txt:(}0.206{txt:)}}    {ralign 12:{txt:(}0.206{txt:)}}    {ralign 12:{txt:(}0.188{txt:)}}    {ralign 12:{txt:(}0.166{txt:)}}   {txt}
{txt}November 2022       {res}       -3.694***       -3.764***       -2.136***       -2.074***{txt}
                    {res} {ralign 12:{txt:(}0.158{txt:)}}    {ralign 12:{txt:(}0.179{txt:)}}    {ralign 12:{txt:(}0.155{txt:)}}    {ralign 12:{txt:(}0.149{txt:)}}   {txt}
{txt}January 2024        {res}       -3.761***       -3.833***       -2.164***       -2.181***{txt}
                    {res} {ralign 12:{txt:(}0.151{txt:)}}    {ralign 12:{txt:(}0.190{txt:)}}    {ralign 12:{txt:(}0.145{txt:)}}    {ralign 12:{txt:(}0.159{txt:)}}   {txt}
{txt}Covid # datewave=722{res}        0.000           0.000           0.000           0.000   {txt}
                    {res} {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}   {txt}
{txt}Threat (Climate) $~2{res}        0.536**         0.735***        0.091          -0.215   {txt}
                    {res} {ralign 12:{txt:(}0.238{txt:)}}    {ralign 12:{txt:(}0.252{txt:)}}    {ralign 12:{txt:(}0.213{txt:)}}    {ralign 12:{txt:(}0.207{txt:)}}   {txt}
{txt}Threat (Climate) $~b{res}        0.550**         0.515**         0.074          -0.083   {txt}
                    {res} {ralign 12:{txt:(}0.228{txt:)}}    {ralign 12:{txt:(}0.236{txt:)}}    {ralign 12:{txt:(}0.209{txt:)}}    {ralign 12:{txt:(}0.203{txt:)}}   {txt}
{txt}Threat (Climate) $~ {res}        1.250***        1.430***        0.383           0.179   {txt}
                    {res} {ralign 12:{txt:(}0.249{txt:)}}    {ralign 12:{txt:(}0.253{txt:)}}    {ralign 12:{txt:(}0.234{txt:)}}    {ralign 12:{txt:(}0.217{txt:)}}   {txt}
{txt}Threat (Climate) $~m{res}        1.901***        2.168***        0.669**         0.647***{txt}
                    {res} {ralign 12:{txt:(}0.302{txt:)}}    {ralign 12:{txt:(}0.296{txt:)}}    {ralign 12:{txt:(}0.277{txt:)}}    {ralign 12:{txt:(}0.251{txt:)}}   {txt}
{txt}Threat (Climate) $~b{res}        2.859***        2.999***        1.322***        1.193***{txt}
                    {res} {ralign 12:{txt:(}0.232{txt:)}}    {ralign 12:{txt:(}0.259{txt:)}}    {ralign 12:{txt:(}0.223{txt:)}}    {ralign 12:{txt:(}0.222{txt:)}}   {txt}
{txt}Threat (Climate) $~r{res}        2.541***        2.514***        1.290***        1.106***{txt}
                    {res} {ralign 12:{txt:(}0.219{txt:)}}    {ralign 12:{txt:(}0.265{txt:)}}    {ralign 12:{txt:(}0.204{txt:)}}    {ralign 12:{txt:(}0.226{txt:)}}   {txt}
{txt}Threat (Terrorism)~e{res}        0.965***        0.849***        0.203          -0.009   {txt}
                    {res} {ralign 12:{txt:(}0.241{txt:)}}    {ralign 12:{txt:(}0.254{txt:)}}    {ralign 12:{txt:(}0.225{txt:)}}    {ralign 12:{txt:(}0.207{txt:)}}   {txt}
{txt}Threat (Terrorism)~e{res}        1.488***        1.360***        0.490**         0.359*  {txt}
                    {res} {ralign 12:{txt:(}0.237{txt:)}}    {ralign 12:{txt:(}0.246{txt:)}}    {ralign 12:{txt:(}0.215{txt:)}}    {ralign 12:{txt:(}0.205{txt:)}}   {txt}
{txt}Threat (Terrorism)~c{res}        1.105***        1.027***        0.235           0.153   {txt}
                    {res} {ralign 12:{txt:(}0.252{txt:)}}    {ralign 12:{txt:(}0.266{txt:)}}    {ralign 12:{txt:(}0.235{txt:)}}    {ralign 12:{txt:(}0.221{txt:)}}   {txt}
{txt}Threat (Terrorism)~t{res}        2.396***        2.307***        0.908***        0.795***{txt}
                    {res} {ralign 12:{txt:(}0.303{txt:)}}    {ralign 12:{txt:(}0.285{txt:)}}    {ralign 12:{txt:(}0.270{txt:)}}    {ralign 12:{txt:(}0.239{txt:)}}   {txt}
{txt}Threat (Climate) $~b{res}        2.968***        2.832***        1.284***        1.228***{txt}
                    {res} {ralign 12:{txt:(}0.232{txt:)}}    {ralign 12:{txt:(}0.254{txt:)}}    {ralign 12:{txt:(}0.219{txt:)}}    {ralign 12:{txt:(}0.215{txt:)}}   {txt}
{txt}Threat (Climate) $~r{res}        3.096***        3.147***        1.544***        1.683***{txt}
                    {res} {ralign 12:{txt:(}0.218{txt:)}}    {ralign 12:{txt:(}0.258{txt:)}}    {ralign 12:{txt:(}0.202{txt:)}}    {ralign 12:{txt:(}0.218{txt:)}}   {txt}
{txt}{hline 84}
{txt}Individual FE       {res}           No             Yes              No             Yes   {txt}
{txt}N. of observations  {res}        10389            9041           10389            9041   {txt}
{txt}N. of unique respo~s{res}         3525            2177            3525            2177   {txt}
{txt}R$^2$               {res}        0.089           0.512           0.041           0.532   {txt}
{txt}{hline 84}
{txt}Standard errors clustered by individual in parentheses. *p$<$.1; **p$<$.05; ***p$<$.01.

{com}. 
. 
.   
. *****************************************************************************************************************
. *Table F4: Regression estimates for attrition analyses (full models for Figure C1 in the supporting information)*
. *****************************************************************************************************************
. 
. quietly {c -(}
{txt}
{com}. 
. esttab attrition*, replace nonumbers nogaps noomit label noobs nonotes ///
>   order(_cons) coeflabels(_cons "Intercept (January 2020)" 722.datewave "March 2020" 725.datewave "June 2020" 730.datewave "November 2020" 734.datewave "March 2021" 740.datewave "September 2021" 754.datewave "November 2022" 768.datewave "January 2024" age "Age" agesq "Age$^2$" 1.edu "Education (Primary or less)" 2.edu "Education (Secondary)" 3.edu "Education (Tertiary)") ///
>   b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
>   collabels("") scalars("ife Individual FE" "n N. of observations" "unique N. of unique respondents" "r2 R$^2$") ///
>   mgroups("Wave 1 vs rest" "First-time vs repeat", pattern(1 0 1 0) span) ///
>   mlabels("Since wave 1" "Later" "First-time" "Repeater") ///
>   addnotes("Standard errors clustered by individual in parentheses. *p$<$.1; **p$<$.05; ***p$<$.01.")
{res}
{txt}{hline 84}
{txt}                     Wave 1 vs rest                  First-time vs repeat           
{txt}                     Since wave 1           Later      First-time        Repeater   
{txt}                                                                                    
{txt}{hline 84}
{txt}Intercept (Jan~2020){res}        4.790***        5.126***        3.923***        4.165***{txt}
                    {res} {ralign 12:{txt:(}0.027{txt:)}}    {ralign 12:{txt:(}0.032{txt:)}}    {ralign 12:{txt:(}0.157{txt:)}}    {ralign 12:{txt:(}0.200{txt:)}}   {txt}
{txt}datewave=720        {res}        0.000                           0.000                   {txt}
                    {res} {ralign 12:{txt:(}.{txt:)}}                    {ralign 12:{txt:(}.{txt:)}}                   {txt}
{txt}March 2020          {res}        0.271***        0.000           0.395***        0.000   {txt}
                    {res} {ralign 12:{txt:(}0.040{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}0.053{txt:)}}    {ralign 12:{txt:(}.{txt:)}}   {txt}
{txt}June 2020           {res}        0.308***       -0.107**                         0.016   {txt}
                    {res} {ralign 12:{txt:(}0.043{txt:)}}    {ralign 12:{txt:(}0.044{txt:)}}                    {ralign 12:{txt:(}0.042{txt:)}}   {txt}
{txt}November 2020       {res}        0.316***        0.021           0.380***        0.081*  {txt}
                    {res} {ralign 12:{txt:(}0.047{txt:)}}    {ralign 12:{txt:(}0.044{txt:)}}    {ralign 12:{txt:(}0.067{txt:)}}    {ralign 12:{txt:(}0.045{txt:)}}   {txt}
{txt}March 2021          {res}        0.341***       -0.068                           0.023   {txt}
                    {res} {ralign 12:{txt:(}0.047{txt:)}}    {ralign 12:{txt:(}0.046{txt:)}}                    {ralign 12:{txt:(}0.045{txt:)}}   {txt}
{txt}September 2021      {res}        0.231***       -0.102**                        -0.039   {txt}
                    {res} {ralign 12:{txt:(}0.053{txt:)}}    {ralign 12:{txt:(}0.051{txt:)}}                    {ralign 12:{txt:(}0.051{txt:)}}   {txt}
{txt}November 2022       {res}        0.171***       -0.134***        0.162***       -0.070   {txt}
                    {res} {ralign 12:{txt:(}0.051{txt:)}}    {ralign 12:{txt:(}0.048{txt:)}}    {ralign 12:{txt:(}0.060{txt:)}}    {ralign 12:{txt:(}0.047{txt:)}}   {txt}
{txt}January 2024        {res}        0.275***       -0.184***        0.142***       -0.084*  {txt}
                    {res} {ralign 12:{txt:(}0.045{txt:)}}    {ralign 12:{txt:(}0.047{txt:)}}    {ralign 12:{txt:(}0.052{txt:)}}    {ralign 12:{txt:(}0.046{txt:)}}   {txt}
{txt}Man                 {res}                                        0.000           0.000   {txt}
                    {res}                                 {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}   {txt}
{txt}Woman               {res}                                       -0.103**        -0.020   {txt}
                    {res}                                 {ralign 12:{txt:(}0.040{txt:)}}    {ralign 12:{txt:(}0.046{txt:)}}   {txt}
{txt}Age                 {res}                                        0.035***        0.034***{txt}
                    {res}                                 {ralign 12:{txt:(}0.006{txt:)}}    {ralign 12:{txt:(}0.008{txt:)}}   {txt}
{txt}Age$^2$             {res}                                       -0.000***       -0.000***{txt}
                    {res}                                 {ralign 12:{txt:(}0.000{txt:)}}    {ralign 12:{txt:(}0.000{txt:)}}   {txt}
{txt}Primary or less     {res}                                        0.000           0.000   {txt}
                    {res}                                 {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}   {txt}
{txt}Secondary           {res}                                       -0.010           0.115** {txt}
                    {res}                                 {ralign 12:{txt:(}0.051{txt:)}}    {ralign 12:{txt:(}0.051{txt:)}}   {txt}
{txt}Tertiary            {res}                                       -0.032          -0.020   {txt}
                    {res}                                 {ralign 12:{txt:(}0.046{txt:)}}    {ralign 12:{txt:(}0.058{txt:)}}   {txt}
{txt}{hline 84}
{txt}Individual FE       {res}          Yes             Yes              No              No   {txt}
{txt}N. of observations  {res}         4567            5096            3518            7514   {txt}
{txt}N. of unique respo~s{res}          855            1361            3518            2245   {txt}
{txt}R$^2$               {res}        0.609           0.649           0.039           0.014   {txt}
{txt}{hline 84}
{txt}Standard errors clustered by individual in parentheses. *p$<$.1; **p$<$.05; ***p$<$.01.

{com}. 
. 
.   
. ********************************************************************************************************
. *Table F5: Wave-by-initial shift coefficients (full models for Figure D4 in the supporting information)*
. ********************************************************************************************************
. 
. quietly {c -(}
{txt}
{com}. 
. esttab a0 a1, replace se nonumbers keep($coefinter) ///
>         coeflabels(inter_delta_techatt2 "March 2020 $\times$ Initial Shift" inter_delta_techatt3 "Jun 2020 $\times$ Initial Shift" inter_delta_techatt4 "Nov 2020 $\times$ Initial Shift" inter_delta_techatt5 "Mar 2021 $\times$ Initial Shift" inter_delta_techatt6 "Sep 2021 $\times$ Initial Shift" inter_delta_techatt7 "Nov 2022 $\times$ Initial Shift" inter_delta_techatt8 "Jan 2024 $\times$ Initial Shift") ///
>         mtitles("Tech. attitudes (avg)." "Tech. attitudes (avg).") ///
>         scalars("wfe Wave FE" "wbyjanfe Wave-by-Jan 2020 level FE" "n N. of observations") ///
>         b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
>         note("Standard errors clustered by individual in parentheses.")
{res}
{txt}{hline 44}
{txt}             Tech. atti~.    Tech. atti~.   
{txt}{hline 44}
{txt}March 2020~f{res}        1.000           1.000***{txt}
            {res} {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}0.000{txt:)}}   {txt}

{txt}Jun 2020 $~t{res}        0.567***        0.416***{txt}
            {res} {ralign 12:{txt:(}0.041{txt:)}}    {ralign 12:{txt:(}0.042{txt:)}}   {txt}

{txt}Nov 2020 $~t{res}        0.524***        0.364***{txt}
            {res} {ralign 12:{txt:(}0.054{txt:)}}    {ralign 12:{txt:(}0.058{txt:)}}   {txt}

{txt}Mar 2021 $~t{res}        0.535***        0.380***{txt}
            {res} {ralign 12:{txt:(}0.055{txt:)}}    {ralign 12:{txt:(}0.058{txt:)}}   {txt}

{txt}Sep 2021 $~t{res}        0.472***        0.363***{txt}
            {res} {ralign 12:{txt:(}0.056{txt:)}}    {ralign 12:{txt:(}0.058{txt:)}}   {txt}

{txt}Nov 2022 $~t{res}        0.563***        0.406***{txt}
            {res} {ralign 12:{txt:(}0.072{txt:)}}    {ralign 12:{txt:(}0.072{txt:)}}   {txt}

{txt}Jan 2024 $~t{res}        0.548***        0.349***{txt}
            {res} {ralign 12:{txt:(}0.053{txt:)}}    {ralign 12:{txt:(}0.052{txt:)}}   {txt}
{txt}{hline 44}
{txt}N           {res}         4295            4295   {txt}
{txt}Wave FE     {res}          Yes             Yes   {txt}
{txt}Wave-by-Ja~E{res}           No             Yes   {txt}
{txt}N. of obse~s{res}         4295            4295   {txt}
{txt}{hline 44}
{txt}Standard errors clustered by individual in parentheses.
{txt}* p<0.10, ** p<0.05, *** p<0.01

{com}.         
. 
. ********************************************************************************************************************************
. *Table F6: Regression estimates for heterogeneity analyses (full models for top panels Figure E1 in the supporting information)*
. ********************************************************************************************************************************
. 
. quietly {c -(}
{txt}
{com}. 
. esttab htsa*, replace nonumbers nogaps noomit nobase label noobs nonotes ///
>   order(_cons) coeflabels(_cons "Intercept (January 2020)" 722.datewave "March 2020" 725.datewave "June 2020" 730.datewave "November 2020" 734.datewave "March 2021" 740.datewave "September 2021" 754.datewave "November 2022" 768.datewave "January 2024") ///
>   b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
>   collabels("") scalars("ife Individual FE" "n N. of observations" "unique N. of unique respondents" "r2 R$^2$") ///
>   mgroups("Gender" "Age" "Education", pattern(1 0 1 0 1 0 0) span) ///
>   mlabels("Men" "Women" "Under-35" "Over-35" "Primary or less" "Secondary" "Tertiary") ///
>   addnotes("Standard errors clustered by individual in parentheses. *p$<$.1; **p$<$.05; ***p$<$.01.")
{res}
{txt}{hline 132}
{txt}                     Gender                          Age                             Education                                      
{txt}                              Men           Women        Under-35         Over-35    Primary or~s       Secondary        Tertiary   
{txt}                                                                                                                                    
{txt}{hline 132}
{txt}Intercept (Jan~2020){res}        4.884***        4.692***        4.382***        4.910***        4.885***        4.803***        4.645***{txt}
                    {res} {ralign 12:{txt:(}0.040{txt:)}}    {ralign 12:{txt:(}0.035{txt:)}}    {ralign 12:{txt:(}0.049{txt:)}}    {ralign 12:{txt:(}0.031{txt:)}}    {ralign 12:{txt:(}0.047{txt:)}}    {ralign 12:{txt:(}0.046{txt:)}}    {ralign 12:{txt:(}0.040{txt:)}}   {txt}
{txt}March 2020          {res}        0.161***        0.383***        0.474***        0.204***        0.234***        0.209***        0.377***{txt}
                    {res} {ralign 12:{txt:(}0.059{txt:)}}    {ralign 12:{txt:(}0.055{txt:)}}    {ralign 12:{txt:(}0.075{txt:)}}    {ralign 12:{txt:(}0.048{txt:)}}    {ralign 12:{txt:(}0.072{txt:)}}    {ralign 12:{txt:(}0.071{txt:)}}    {ralign 12:{txt:(}0.059{txt:)}}   {txt}
{txt}June 2020           {res}        0.254***        0.362***        0.485***        0.251***        0.281***        0.333***        0.326***{txt}
                    {res} {ralign 12:{txt:(}0.064{txt:)}}    {ralign 12:{txt:(}0.057{txt:)}}    {ralign 12:{txt:(}0.085{txt:)}}    {ralign 12:{txt:(}0.050{txt:)}}    {ralign 12:{txt:(}0.076{txt:)}}    {ralign 12:{txt:(}0.082{txt:)}}    {ralign 12:{txt:(}0.061{txt:)}}   {txt}
{txt}November 2020       {res}        0.228***        0.408***        0.495***        0.261***        0.287***        0.321***        0.354***{txt}
                    {res} {ralign 12:{txt:(}0.071{txt:)}}    {ralign 12:{txt:(}0.058{txt:)}}    {ralign 12:{txt:(}0.088{txt:)}}    {ralign 12:{txt:(}0.054{txt:)}}    {ralign 12:{txt:(}0.082{txt:)}}    {ralign 12:{txt:(}0.085{txt:)}}    {ralign 12:{txt:(}0.066{txt:)}}   {txt}
{txt}March 2021          {res}        0.244***        0.446***        0.608***        0.263***        0.206**         0.528***        0.389***{txt}
                    {res} {ralign 12:{txt:(}0.066{txt:)}}    {ralign 12:{txt:(}0.065{txt:)}}    {ralign 12:{txt:(}0.092{txt:)}}    {ralign 12:{txt:(}0.054{txt:)}}    {ralign 12:{txt:(}0.081{txt:)}}    {ralign 12:{txt:(}0.081{txt:)}}    {ralign 12:{txt:(}0.071{txt:)}}   {txt}
{txt}September 2021      {res}        0.189**         0.264***        0.455***        0.166***        0.158*          0.281**         0.296***{txt}
                    {res} {ralign 12:{txt:(}0.077{txt:)}}    {ralign 12:{txt:(}0.070{txt:)}}    {ralign 12:{txt:(}0.095{txt:)}}    {ralign 12:{txt:(}0.062{txt:)}}    {ralign 12:{txt:(}0.094{txt:)}}    {ralign 12:{txt:(}0.109{txt:)}}    {ralign 12:{txt:(}0.072{txt:)}}   {txt}
{txt}November 2022       {res}        0.061           0.296***        0.457***        0.100*          0.075           0.255***        0.258***{txt}
                    {res} {ralign 12:{txt:(}0.076{txt:)}}    {ralign 12:{txt:(}0.065{txt:)}}    {ralign 12:{txt:(}0.099{txt:)}}    {ralign 12:{txt:(}0.058{txt:)}}    {ralign 12:{txt:(}0.086{txt:)}}    {ralign 12:{txt:(}0.096{txt:)}}    {ralign 12:{txt:(}0.073{txt:)}}   {txt}
{txt}January 2024        {res}        0.140**         0.427***        0.452***        0.224***        0.245***        0.238***        0.351***{txt}
                    {res} {ralign 12:{txt:(}0.065{txt:)}}    {ralign 12:{txt:(}0.061{txt:)}}    {ralign 12:{txt:(}0.097{txt:)}}    {ralign 12:{txt:(}0.051{txt:)}}    {ralign 12:{txt:(}0.073{txt:)}}    {ralign 12:{txt:(}0.087{txt:)}}    {ralign 12:{txt:(}0.076{txt:)}}   {txt}
{txt}{hline 132}
{txt}Individual FE       {res}          Yes             Yes             Yes             Yes             Yes             Yes             Yes   {txt}
{txt}N. of observations  {res}         2408            2159             988            3579            2017            1149            1401   {txt}
{txt}N. of unique respo~s{res}          428             427             208             647             371             227             257   {txt}
{txt}R$^2$               {res}        0.604           0.622           0.591           0.609           0.553           0.622           0.708   {txt}
{txt}{hline 132}
{txt}Standard errors clustered by individual in parentheses. *p$<$.1; **p$<$.05; ***p$<$.01.

{com}. 
. 
.   
. **************************************************************************************************************************************
. *Table F7: Regression estimates for heterogeneity analyses (full models for bottom panels in Figure E1 in the supporting information)*
. **************************************************************************************************************************************
. 
. quietly {c -(}
{txt}
{com}. 
. esttab htsb*, replace nonumbers nogaps noomit nobase label noobs nonotes ///
>   order(_cons) coeflabels(_cons "Intercept (January 2020)" 722.datewave "March 2020" 725.datewave "June 2020" 730.datewave "November 2020" 734.datewave "March 2021" 740.datewave "September 2021" 754.datewave "November 2022" 768.datewave "January 2024") ///
>   b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
>   collabels("") scalars("ife Individual FE" "n N. of observations" "unique N. of unique respondents" "r2 R$^2$") ///
>   mgroups("Political interest" "Incumbent support" "Ideology", pattern(1 0 1 0 1 0 0) span) ///
>   mlabels("Low" "High" "No" "Yes" "Left" "Center" "Right") ///
>   addnotes("Standard errors clustered by individual in parentheses. *p$<$.1; **p$<$.05; ***p$<$.01.")
{res}
{txt}{hline 132}
{txt}                     Political interest              Incumbent support               Ideology                                       
{txt}                              Low            High              No             Yes            Left          Center           Right   
{txt}                                                                                                                                    
{txt}{hline 132}
{txt}Intercept (Jan~2020){res}        4.953***        4.616***        4.840***        4.595***        4.419***        4.919***        4.945***{txt}
                    {res} {ralign 12:{txt:(}0.040{txt:)}}    {ralign 12:{txt:(}0.034{txt:)}}    {ralign 12:{txt:(}0.030{txt:)}}    {ralign 12:{txt:(}0.057{txt:)}}    {ralign 12:{txt:(}0.036{txt:)}}    {ralign 12:{txt:(}0.058{txt:)}}    {ralign 12:{txt:(}0.061{txt:)}}   {txt}
{txt}March 2020          {res}        0.216***        0.330***        0.253***        0.347***        0.294***        0.380***        0.319***{txt}
                    {res} {ralign 12:{txt:(}0.061{txt:)}}    {ralign 12:{txt:(}0.052{txt:)}}    {ralign 12:{txt:(}0.046{txt:)}}    {ralign 12:{txt:(}0.083{txt:)}}    {ralign 12:{txt:(}0.057{txt:)}}    {ralign 12:{txt:(}0.091{txt:)}}    {ralign 12:{txt:(}0.095{txt:)}}   {txt}
{txt}June 2020           {res}        0.270***        0.348***        0.314***        0.287***        0.285***        0.337***        0.567***{txt}
                    {res} {ralign 12:{txt:(}0.066{txt:)}}    {ralign 12:{txt:(}0.055{txt:)}}    {ralign 12:{txt:(}0.048{txt:)}}    {ralign 12:{txt:(}0.098{txt:)}}    {ralign 12:{txt:(}0.058{txt:)}}    {ralign 12:{txt:(}0.112{txt:)}}    {ralign 12:{txt:(}0.092{txt:)}}   {txt}
{txt}November 2020       {res}        0.255***        0.380***        0.347***        0.198           0.256***        0.438***        0.563***{txt}
                    {res} {ralign 12:{txt:(}0.068{txt:)}}    {ralign 12:{txt:(}0.063{txt:)}}    {ralign 12:{txt:(}0.050{txt:)}}    {ralign 12:{txt:(}0.120{txt:)}}    {ralign 12:{txt:(}0.067{txt:)}}    {ralign 12:{txt:(}0.096{txt:)}}    {ralign 12:{txt:(}0.107{txt:)}}   {txt}
{txt}March 2021          {res}        0.279***        0.404***        0.365***        0.249**         0.315***        0.267**         0.599***{txt}
                    {res} {ralign 12:{txt:(}0.071{txt:)}}    {ralign 12:{txt:(}0.061{txt:)}}    {ralign 12:{txt:(}0.052{txt:)}}    {ralign 12:{txt:(}0.104{txt:)}}    {ralign 12:{txt:(}0.064{txt:)}}    {ralign 12:{txt:(}0.104{txt:)}}    {ralign 12:{txt:(}0.105{txt:)}}   {txt}
{txt}September 2021      {res}        0.108           0.352***        0.250***        0.156           0.245***        0.104           0.510***{txt}
                    {res} {ralign 12:{txt:(}0.081{txt:)}}    {ralign 12:{txt:(}0.070{txt:)}}    {ralign 12:{txt:(}0.057{txt:)}}    {ralign 12:{txt:(}0.143{txt:)}}    {ralign 12:{txt:(}0.073{txt:)}}    {ralign 12:{txt:(}0.142{txt:)}}    {ralign 12:{txt:(}0.119{txt:)}}   {txt}
{txt}November 2022       {res}        0.152**         0.190***        0.182***        0.134           0.165**         0.183*          0.363***{txt}
                    {res} {ralign 12:{txt:(}0.077{txt:)}}    {ralign 12:{txt:(}0.066{txt:)}}    {ralign 12:{txt:(}0.058{txt:)}}    {ralign 12:{txt:(}0.107{txt:)}}    {ralign 12:{txt:(}0.066{txt:)}}    {ralign 12:{txt:(}0.106{txt:)}}    {ralign 12:{txt:(}0.128{txt:)}}   {txt}
{txt}January 2024        {res}        0.224***        0.330***        0.309***        0.148           0.213***        0.348***        0.472***{txt}
                    {res} {ralign 12:{txt:(}0.069{txt:)}}    {ralign 12:{txt:(}0.059{txt:)}}    {ralign 12:{txt:(}0.051{txt:)}}    {ralign 12:{txt:(}0.101{txt:)}}    {ralign 12:{txt:(}0.070{txt:)}}    {ralign 12:{txt:(}0.095{txt:)}}    {ralign 12:{txt:(}0.095{txt:)}}   {txt}
{txt}{hline 132}
{txt}Individual FE       {res}          Yes             Yes             Yes             Yes             Yes             Yes             Yes   {txt}
{txt}N. of observations  {res}         2365            2202            3628             939            1994             758             876   {txt}
{txt}N. of unique respo~s{res}          459             396             676             179             365             141             160   {txt}
{txt}R$^2$               {res}        0.537           0.674           0.627           0.518           0.602           0.542           0.576   {txt}
{txt}{hline 132}
{txt}Standard errors clustered by individual in parentheses. *p$<$.1; **p$<$.05; ***p$<$.01.

{com}. 
. 
.   
. ***********************************************************************************************************************************
. *Table F8: Regression estimates for heterogeneity analyses (full models for top panels in Figure E2 in the supporting information)*
. ***********************************************************************************************************************************
. 
. quietly {c -(}
{txt}
{com}. 
. esttab htea*, replace nonumbers nogaps noomit nobase label noobs nonotes ///
>   order(_cons) coeflabels(_cons "Intercept (January 2020)" 722.datewave "March 2020" 725.datewave "June 2020" 730.datewave "November 2020" 734.datewave "March 2021" 740.datewave "September 2021" 754.datewave "November 2022" 768.datewave "January 2024") ///
>   b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
>   collabels("") scalars("ife Individual FE" "n N. of observations" "unique N. of unique respondents" "r2 R$^2$") ///
>   mgroups("Health exposure" "Health risk (obj)" "Health risk (subj)", pattern(1 0 1 0 1) span) ///
>   mlabels("Non-infected" "Infected" "Under-60" "Over-60" "") ///
>   addnotes("Standard errors clustered by individual in parentheses. *p$<$.1; **p$<$.05; ***p$<$.01.")
{res}
{txt}{hline 100}
{txt}                     Health exposure                 Health risk (obj)               Health risk (~)
{txt}                     Non-infected        Infected        Under-60         Over-60                   
{txt}                                                                                                    
{txt}{hline 100}
{txt}Intercept (Jan~2020){res}        4.809***        4.771***        4.774***        4.855***        4.726***{txt}
                    {res} {ralign 12:{txt:(}0.031{txt:)}}    {ralign 12:{txt:(}0.206{txt:)}}    {ralign 12:{txt:(}0.029{txt:)}}    {ralign 12:{txt:(}0.065{txt:)}}    {ralign 12:{txt:(}0.074{txt:)}}   {txt}
{txt}March 2020          {res}        0.295***        0.195           0.314***        0.098           0.192** {txt}
                    {res} {ralign 12:{txt:(}0.039{txt:)}}    {ralign 12:{txt:(}0.209{txt:)}}    {ralign 12:{txt:(}0.044{txt:)}}    {ralign 12:{txt:(}0.100{txt:)}}    {ralign 12:{txt:(}0.082{txt:)}}   {txt}
{txt}June 2020           {res}        0.276***        0.169           0.321***        0.254**         0.216** {txt}
                    {res} {ralign 12:{txt:(}0.041{txt:)}}    {ralign 12:{txt:(}0.233{txt:)}}    {ralign 12:{txt:(}0.047{txt:)}}    {ralign 12:{txt:(}0.104{txt:)}}    {ralign 12:{txt:(}0.089{txt:)}}   {txt}
{txt}November 2020       {res}        0.328***        0.297           0.369***        0.117           0.390***{txt}
                    {res} {ralign 12:{txt:(}0.042{txt:)}}    {ralign 12:{txt:(}0.220{txt:)}}    {ralign 12:{txt:(}0.050{txt:)}}    {ralign 12:{txt:(}0.119{txt:)}}    {ralign 12:{txt:(}0.090{txt:)}}   {txt}
{txt}March 2021          {res}        0.282***        0.287           0.364***        0.253**         0.268***{txt}
                    {res} {ralign 12:{txt:(}0.043{txt:)}}    {ralign 12:{txt:(}0.220{txt:)}}    {ralign 12:{txt:(}0.050{txt:)}}    {ralign 12:{txt:(}0.116{txt:)}}    {ralign 12:{txt:(}0.094{txt:)}}   {txt}
{txt}September 2021      {res}        0.216***        0.236           0.287***        0.032           0.177*  {txt}
                    {res} {ralign 12:{txt:(}0.047{txt:)}}    {ralign 12:{txt:(}0.224{txt:)}}    {ralign 12:{txt:(}0.057{txt:)}}    {ralign 12:{txt:(}0.133{txt:)}}    {ralign 12:{txt:(}0.102{txt:)}}   {txt}
{txt}November 2022       {res}        0.235***        0.178           0.211***        0.039           0.222** {txt}
                    {res} {ralign 12:{txt:(}0.055{txt:)}}    {ralign 12:{txt:(}0.212{txt:)}}    {ralign 12:{txt:(}0.056{txt:)}}    {ralign 12:{txt:(}0.115{txt:)}}    {ralign 12:{txt:(}0.095{txt:)}}   {txt}
{txt}January 2024        {res}        0.199***        0.183           0.298***        0.187*          0.225** {txt}
                    {res} {ralign 12:{txt:(}0.057{txt:)}}    {ralign 12:{txt:(}0.211{txt:)}}    {ralign 12:{txt:(}0.050{txt:)}}    {ralign 12:{txt:(}0.104{txt:)}}    {ralign 12:{txt:(}0.094{txt:)}}   {txt}
{txt}subjserious         {res}                                                                        0.009   {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.015{txt:)}}   {txt}
{txt}datewave=722 # sub~s{res}                                                                        0.023   {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.016{txt:)}}   {txt}
{txt}datewave=725 # sub~s{res}                                                                        0.014   {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.018{txt:)}}   {txt}
{txt}datewave=730 # sub~s{res}                                                                       -0.009   {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.017{txt:)}}   {txt}
{txt}datewave=734 # sub~s{res}                                                                        0.007   {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.018{txt:)}}   {txt}
{txt}datewave=740 # sub~s{res}                                                                        0.018   {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.020{txt:)}}   {txt}
{txt}datewave=754 # sub~s{res}                                                                       -0.006   {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.019{txt:)}}   {txt}
{txt}datewave=768 # sub~s{res}                                                                       -0.006   {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.019{txt:)}}   {txt}
{txt}{hline 100}
{txt}Individual FE       {res}          Yes             Yes             Yes             Yes             Yes   {txt}
{txt}N. of observations  {res}         7177            1860            3563            1004            7916   {txt}
{txt}N. of unique respo~s{res}         1793             745             678             177            1952   {txt}
{txt}R$^2$               {res}        0.632           0.734           0.608           0.616           0.638   {txt}
{txt}{hline 100}
{txt}Standard errors clustered by individual in parentheses. *p$<$.1; **p$<$.05; ***p$<$.01.

{com}. 
.   
. 
. ********************************************************************************************************
. *Table F9: Regression estimates for heterogeneity analyses (full models for bottom panels in Figure E2)*
. ********************************************************************************************************
. 
. quietly {c -(}
{txt}
{com}. 
. esttab hteb*, replace nonumbers nogaps noomit nobase label noobs nonotes ///
>   order(_cons) coeflabels(_cons "Intercept (January 2020)" 722.datewave "March 2020" 725.datewave "June 2020" 730.datewave "November 2020" 734.datewave "March 2021" 740.datewave "September 2021" 754.datewave "November 2022" 768.datewave "January 2024") ///
>   b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
>   collabels("") scalars("ife Individual FE" "n N. of observations" "unique N. of unique respondents" "r2 R$^2$") ///
>   mgroups("Economic risk (subj)" "Health risk (pred)" "Economic risk (pred)", pattern(1 0 1 1) span) ///
>   mlabels("Low" "High" "" "") ///
>   addnotes("Standard errors clustered by individual in parentheses. *p$<$.1; **p$<$.05; ***p$<$.01.")
{res}
{txt}{hline 84}
{txt}                     Economic risk (subj)            Health risk (~) Economic risk~)
{txt}                              Low            High                                   
{txt}                                                                                    
{txt}{hline 84}
{txt}Intercept (Jan~2020){res}        4.791***        4.805***        4.366***        4.454***{txt}
                    {res} {ralign 12:{txt:(}0.065{txt:)}}    {ralign 12:{txt:(}0.034{txt:)}}    {ralign 12:{txt:(}0.232{txt:)}}    {ralign 12:{txt:(}0.352{txt:)}}   {txt}
{txt}March 2020          {res}        0.141*          0.349***        0.517**         0.619*  {txt}
                    {res} {ralign 12:{txt:(}0.078{txt:)}}    {ralign 12:{txt:(}0.042{txt:)}}    {ralign 12:{txt:(}0.213{txt:)}}    {ralign 12:{txt:(}0.370{txt:)}}   {txt}
{txt}June 2020           {res}        0.126           0.329***        0.467**         0.518   {txt}
                    {res} {ralign 12:{txt:(}0.082{txt:)}}    {ralign 12:{txt:(}0.045{txt:)}}    {ralign 12:{txt:(}0.224{txt:)}}    {ralign 12:{txt:(}0.399{txt:)}}   {txt}
{txt}November 2020       {res}        0.191**         0.412***        0.780***        0.710*  {txt}
                    {res} {ralign 12:{txt:(}0.084{txt:)}}    {ralign 12:{txt:(}0.047{txt:)}}    {ralign 12:{txt:(}0.232{txt:)}}    {ralign 12:{txt:(}0.400{txt:)}}   {txt}
{txt}March 2021          {res}        0.112           0.376***        0.710***        0.779** {txt}
                    {res} {ralign 12:{txt:(}0.080{txt:)}}    {ralign 12:{txt:(}0.049{txt:)}}    {ralign 12:{txt:(}0.209{txt:)}}    {ralign 12:{txt:(}0.394{txt:)}}   {txt}
{txt}September 2021      {res}        0.128           0.278***        0.563**         0.428   {txt}
                    {res} {ralign 12:{txt:(}0.085{txt:)}}    {ralign 12:{txt:(}0.056{txt:)}}    {ralign 12:{txt:(}0.230{txt:)}}    {ralign 12:{txt:(}0.470{txt:)}}   {txt}
{txt}November 2022       {res}        0.149*          0.203***        0.698***        0.538   {txt}
                    {res} {ralign 12:{txt:(}0.082{txt:)}}    {ralign 12:{txt:(}0.052{txt:)}}    {ralign 12:{txt:(}0.230{txt:)}}    {ralign 12:{txt:(}0.455{txt:)}}   {txt}
{txt}January 2024        {res}        0.077           0.251***        0.714***        0.497   {txt}
                    {res} {ralign 12:{txt:(}0.077{txt:)}}    {ralign 12:{txt:(}0.054{txt:)}}    {ralign 12:{txt:(}0.226{txt:)}}    {ralign 12:{txt:(}0.433{txt:)}}   {txt}
{txt}Fitted values       {res}                                        0.098*                  {txt}
                    {res}                                 {ralign 12:{txt:(}0.053{txt:)}}                   {txt}
{txt}datewave=722 # Fit~s{res}                                       -0.056                   {txt}
                    {res}                                 {ralign 12:{txt:(}0.048{txt:)}}                   {txt}
{txt}datewave=725 # Fit~s{res}                                       -0.049                   {txt}
                    {res}                                 {ralign 12:{txt:(}0.050{txt:)}}                   {txt}
{txt}datewave=730 # Fit~s{res}                                       -0.101*                  {txt}
                    {res}                                 {ralign 12:{txt:(}0.053{txt:)}}                   {txt}
{txt}datewave=734 # Fit~s{res}                                       -0.095**                 {txt}
                    {res}                                 {ralign 12:{txt:(}0.047{txt:)}}                   {txt}
{txt}datewave=740 # Fit~s{res}                                       -0.075                   {txt}
                    {res}                                 {ralign 12:{txt:(}0.052{txt:)}}                   {txt}
{txt}datewave=754 # Fit~s{res}                                       -0.113**                 {txt}
                    {res}                                 {ralign 12:{txt:(}0.051{txt:)}}                   {txt}
{txt}datewave=768 # Fit~s{res}                                       -0.115**                 {txt}
                    {res}                                 {ralign 12:{txt:(}0.050{txt:)}}                   {txt}
{txt}Fitted values       {res}                                                        0.156   {txt}
                    {res}                                                 {ralign 12:{txt:(}0.166{txt:)}}   {txt}
{txt}datewave=722 # Fit~s{res}                                                       -0.150   {txt}
                    {res}                                                 {ralign 12:{txt:(}0.173{txt:)}}   {txt}
{txt}datewave=725 # Fit~s{res}                                                       -0.117   {txt}
                    {res}                                                 {ralign 12:{txt:(}0.186{txt:)}}   {txt}
{txt}datewave=730 # Fit~s{res}                                                       -0.173   {txt}
                    {res}                                                 {ralign 12:{txt:(}0.187{txt:)}}   {txt}
{txt}datewave=734 # Fit~s{res}                                                       -0.224   {txt}
                    {res}                                                 {ralign 12:{txt:(}0.183{txt:)}}   {txt}
{txt}datewave=740 # Fit~s{res}                                                       -0.093   {txt}
                    {res}                                                 {ralign 12:{txt:(}0.217{txt:)}}   {txt}
{txt}datewave=754 # Fit~s{res}                                                       -0.160   {txt}
                    {res}                                                 {ralign 12:{txt:(}0.210{txt:)}}   {txt}
{txt}datewave=768 # Fit~s{res}                                                       -0.141   {txt}
                    {res}                                                 {ralign 12:{txt:(}0.200{txt:)}}   {txt}
{txt}{hline 84}
{txt}Individual FE       {res}          Yes             Yes             Yes             Yes   {txt}
{txt}N. of observations  {res}         2900            5952            7936            9575   {txt}
{txt}N. of unique respo~s{res}          902            1577            1724            2187   {txt}
{txt}R$^2$               {res}        0.694           0.627           0.628           0.630   {txt}
{txt}{hline 84}
{txt}Standard errors clustered by individual in parentheses. *p$<$.1; **p$<$.05; ***p$<$.01.

{com}.   
. 
.   
. ******************************************************************************************************
. *Table F10: Regression estimates for heterogeneity analyses (full models for top panels in Figure E3)*
. ******************************************************************************************************
. 
. quietly {c -(}
{txt}
{com}. 
. esttab hlsa*, replace nonumbers nogaps noomit label noobs nonotes ///
>   order(_cons) coeflabels(_cons "Intercept" 722.datewave "March 2020" 725.datewave "June 2020" 730.datewave "November 2020" 734.datewave "March 2021" 740.datewave "September 2021" 754.datewave "November 2022" 768.datewave "January 2024" 1.threatcovid "Threat (Covid)") ///
>   b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
>   collabels("") scalars("ife Individual FE" "n N. of observations" "unique N. of unique respondents" "r2 R$^2$") ///
>   mgroups("Gender" "Age" "Education", pattern(1 0 1 0 1 0 0) span) ///
>   mlabels("Men" "Women" "Under-35" "Over-35" "Primary or less" "Secondary" "Tertiary") ///
>   addnotes("Standard errors clustered by individual in parentheses. *p$<$.1; **p$<$.05; ***p$<$.01.")
{res}
{txt}{hline 132}
{txt}                     Gender                          Age                             Education                                      
{txt}                              Men           Women        Under-35         Over-35    Primary or~s       Secondary        Tertiary   
{txt}                                                                                                                                    
{txt}{hline 132}
{txt}Intercept           {res}        5.912***        5.686***        5.735***        5.824***        6.084***        5.430***        5.690***{txt}
                    {res} {ralign 12:{txt:(}0.162{txt:)}}    {ralign 12:{txt:(}0.156{txt:)}}    {ralign 12:{txt:(}0.232{txt:)}}    {ralign 12:{txt:(}0.128{txt:)}}    {ralign 12:{txt:(}0.175{txt:)}}    {ralign 12:{txt:(}0.212{txt:)}}    {ralign 12:{txt:(}0.203{txt:)}}   {txt}
{txt}threatcovid=0       {res}        0.000           0.000           0.000           0.000           0.000           0.000           0.000   {txt}
                    {res} {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}   {txt}
{txt}Threat (Covid)      {res}        2.602***        2.896***        2.425***        2.840***        2.384***        3.307***        2.863***{txt}
                    {res} {ralign 12:{txt:(}0.324{txt:)}}    {ralign 12:{txt:(}0.282{txt:)}}    {ralign 12:{txt:(}0.435{txt:)}}    {ralign 12:{txt:(}0.249{txt:)}}    {ralign 12:{txt:(}0.317{txt:)}}    {ralign 12:{txt:(}0.480{txt:)}}    {ralign 12:{txt:(}0.377{txt:)}}   {txt}
{txt}March 2020          {res}        0.000           0.000           0.000           0.000           0.000           0.000           0.000   {txt}
                    {res} {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}   {txt}
{txt}June 2020           {res}       -0.930***       -1.065***       -1.018***       -1.005***       -0.828***       -0.927***       -1.302***{txt}
                    {res} {ralign 12:{txt:(}0.228{txt:)}}    {ralign 12:{txt:(}0.222{txt:)}}    {ralign 12:{txt:(}0.324{txt:)}}    {ralign 12:{txt:(}0.182{txt:)}}    {ralign 12:{txt:(}0.254{txt:)}}    {ralign 12:{txt:(}0.327{txt:)}}    {ralign 12:{txt:(}0.253{txt:)}}   {txt}
{txt}November 2020       {res}       -0.905***       -0.363          -0.748**        -0.628***       -0.407          -0.548          -1.046***{txt}
                    {res} {ralign 12:{txt:(}0.242{txt:)}}    {ralign 12:{txt:(}0.230{txt:)}}    {ralign 12:{txt:(}0.337{txt:)}}    {ralign 12:{txt:(}0.192{txt:)}}    {ralign 12:{txt:(}0.264{txt:)}}    {ralign 12:{txt:(}0.341{txt:)}}    {ralign 12:{txt:(}0.273{txt:)}}   {txt}
{txt}March 2021          {res}       -1.354***       -0.953***       -0.934**        -1.236***       -1.347***       -0.633*         -1.325***{txt}
                    {res} {ralign 12:{txt:(}0.271{txt:)}}    {ralign 12:{txt:(}0.269{txt:)}}    {ralign 12:{txt:(}0.371{txt:)}}    {ralign 12:{txt:(}0.221{txt:)}}    {ralign 12:{txt:(}0.293{txt:)}}    {ralign 12:{txt:(}0.368{txt:)}}    {ralign 12:{txt:(}0.346{txt:)}}   {txt}
{txt}September 2021      {res}       -0.721***       -0.476           0.121          -0.775***       -0.965***       -0.406          -0.274   {txt}
                    {res} {ralign 12:{txt:(}0.274{txt:)}}    {ralign 12:{txt:(}0.294{txt:)}}    {ralign 12:{txt:(}0.378{txt:)}}    {ralign 12:{txt:(}0.230{txt:)}}    {ralign 12:{txt:(}0.316{txt:)}}    {ralign 12:{txt:(}0.408{txt:)}}    {ralign 12:{txt:(}0.337{txt:)}}   {txt}
{txt}November 2022       {res}       -1.229***       -1.000***       -1.077**        -1.121***       -1.204***       -0.778**        -1.255***{txt}
                    {res} {ralign 12:{txt:(}0.279{txt:)}}    {ralign 12:{txt:(}0.280{txt:)}}    {ralign 12:{txt:(}0.475{txt:)}}    {ralign 12:{txt:(}0.220{txt:)}}    {ralign 12:{txt:(}0.288{txt:)}}    {ralign 12:{txt:(}0.392{txt:)}}    {ralign 12:{txt:(}0.403{txt:)}}   {txt}
{txt}January 2024        {res}       -1.097***       -0.942***       -0.526          -1.121***       -0.869***       -1.096***       -1.271***{txt}
                    {res} {ralign 12:{txt:(}0.268{txt:)}}    {ralign 12:{txt:(}0.281{txt:)}}    {ralign 12:{txt:(}0.448{txt:)}}    {ralign 12:{txt:(}0.215{txt:)}}    {ralign 12:{txt:(}0.287{txt:)}}    {ralign 12:{txt:(}0.372{txt:)}}    {ralign 12:{txt:(}0.375{txt:)}}   {txt}
{txt}threatcovid=0 # ~722{res}        0.000           0.000           0.000           0.000           0.000           0.000           0.000   {txt}
                    {res} {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}   {txt}
{txt}threatcovid=1 # ~725{res}       -1.111**        -0.560          -0.492          -0.937***       -0.719          -1.536**        -0.497   {txt}
                    {res} {ralign 12:{txt:(}0.433{txt:)}}    {ralign 12:{txt:(}0.398{txt:)}}    {ralign 12:{txt:(}0.535{txt:)}}    {ralign 12:{txt:(}0.349{txt:)}}    {ralign 12:{txt:(}0.468{txt:)}}    {ralign 12:{txt:(}0.630{txt:)}}    {ralign 12:{txt:(}0.461{txt:)}}   {txt}
{txt}threatcovid=1 # ~730{res}       -1.291***       -1.431***       -1.278**        -1.346***       -1.652***       -2.299***       -0.031   {txt}
                    {res} {ralign 12:{txt:(}0.443{txt:)}}    {ralign 12:{txt:(}0.405{txt:)}}    {ralign 12:{txt:(}0.540{txt:)}}    {ralign 12:{txt:(}0.356{txt:)}}    {ralign 12:{txt:(}0.462{txt:)}}    {ralign 12:{txt:(}0.683{txt:)}}    {ralign 12:{txt:(}0.434{txt:)}}   {txt}
{txt}threatcovid=1 # ~734{res}       -0.975**        -1.279***       -1.532**        -1.047***       -0.949*         -1.660**        -0.980*  {txt}
                    {res} {ralign 12:{txt:(}0.467{txt:)}}    {ralign 12:{txt:(}0.451{txt:)}}    {ralign 12:{txt:(}0.637{txt:)}}    {ralign 12:{txt:(}0.375{txt:)}}    {ralign 12:{txt:(}0.520{txt:)}}    {ralign 12:{txt:(}0.650{txt:)}}    {ralign 12:{txt:(}0.545{txt:)}}   {txt}
{txt}threatcovid=1 # ~740{res}       -2.201***       -2.387***       -2.674***       -2.240***       -1.907***       -2.801***       -2.479***{txt}
                    {res} {ralign 12:{txt:(}0.516{txt:)}}    {ralign 12:{txt:(}0.513{txt:)}}    {ralign 12:{txt:(}0.770{txt:)}}    {ralign 12:{txt:(}0.415{txt:)}}    {ralign 12:{txt:(}0.542{txt:)}}    {ralign 12:{txt:(}0.957{txt:)}}    {ralign 12:{txt:(}0.562{txt:)}}   {txt}
{txt}threatcovid=1 # ~754{res}       -2.559***       -3.333***       -2.085***       -3.154***       -3.000***       -3.694***       -2.369***{txt}
                    {res} {ralign 12:{txt:(}0.470{txt:)}}    {ralign 12:{txt:(}0.478{txt:)}}    {ralign 12:{txt:(}0.739{txt:)}}    {ralign 12:{txt:(}0.381{txt:)}}    {ralign 12:{txt:(}0.487{txt:)}}    {ralign 12:{txt:(}0.760{txt:)}}    {ralign 12:{txt:(}0.607{txt:)}}   {txt}
{txt}threatcovid=1 # ~768{res}       -2.700***       -2.954***       -2.878***       -2.842***       -2.539***       -3.665***       -2.486***{txt}
                    {res} {ralign 12:{txt:(}0.509{txt:)}}    {ralign 12:{txt:(}0.477{txt:)}}    {ralign 12:{txt:(}0.783{txt:)}}    {ralign 12:{txt:(}0.394{txt:)}}    {ralign 12:{txt:(}0.511{txt:)}}    {ralign 12:{txt:(}0.691{txt:)}}    {ralign 12:{txt:(}0.670{txt:)}}   {txt}
{txt}{hline 132}
{txt}Individual FE       {res}          Yes             Yes             Yes             Yes             Yes             Yes             Yes   {txt}
{txt}N. of observations  {res}         1994            1759             784            2969            1683             935            1135   {txt}
{txt}N. of unique respo~s{res}          390             381             175             596             339             201             231   {txt}
{txt}R$^2$               {res}        0.478           0.495           0.491           0.485           0.482           0.479           0.510   {txt}
{txt}{hline 132}
{txt}Standard errors clustered by individual in parentheses. *p$<$.1; **p$<$.05; ***p$<$.01.

{com}. 
. 
.   
. *********************************************************************************************************
. *Table F11: Regression estimates for heterogeneity analyses (full models for bottom panels in Figure E3)*
. *********************************************************************************************************
. 
. quietly {c -(}
{txt}
{com}. 
. esttab hlsb*, replace nonumbers nogaps noomit label noobs nonotes ///
>   order(_cons) coeflabels(_cons "Intercept" 722.datewave "March 2020" 725.datewave "June 2020" 730.datewave "November 2020" 734.datewave "March 2021" 740.datewave "September 2021" 754.datewave "November 2022" 768.datewave "January 2024") ///
>   b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
>   collabels("") scalars("ife Individual FE" "n N. of observations" "unique N. of unique respondents" "r2 R$^2$") ///
>   mgroups("Political interest" "Incumbent support" "Ideology", pattern(1 0 1 0 1 0 0) span) ///
>   mlabels("Low" "High" "No" "Yes" "Left" "Center" "Right") ///
>   addnotes("Standard errors clustered by individual in parentheses. *p$<$.1; **p$<$.05; ***p$<$.01.")
{res}
{txt}{hline 132}
{txt}                     Political interest              Incumbent support               Ideology                                       
{txt}                              Low            High              No             Yes            Left          Center           Right   
{txt}                                                                                                                                    
{txt}{hline 132}
{txt}Intercept           {res}        5.884***        5.720***        5.718***        6.146***        5.739***        5.429***        6.415***{txt}
                    {res} {ralign 12:{txt:(}0.151{txt:)}}    {ralign 12:{txt:(}0.169{txt:)}}    {ralign 12:{txt:(}0.126{txt:)}}    {ralign 12:{txt:(}0.246{txt:)}}    {ralign 12:{txt:(}0.174{txt:)}}    {ralign 12:{txt:(}0.288{txt:)}}    {ralign 12:{txt:(}0.280{txt:)}}   {txt}
{txt}threatcovid=0       {res}        0.000           0.000           0.000           0.000           0.000           0.000           0.000   {txt}
                    {res} {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}   {txt}
{txt}threatcovid=1       {res}        2.616***        2.888***        2.865***        2.283***        2.719***        3.771***        2.409***{txt}
                    {res} {ralign 12:{txt:(}0.296{txt:)}}    {ralign 12:{txt:(}0.314{txt:)}}    {ralign 12:{txt:(}0.242{txt:)}}    {ralign 12:{txt:(}0.476{txt:)}}    {ralign 12:{txt:(}0.310{txt:)}}    {ralign 12:{txt:(}0.543{txt:)}}    {ralign 12:{txt:(}0.569{txt:)}}   {txt}
{txt}March 2020          {res}        0.000           0.000           0.000           0.000           0.000           0.000           0.000   {txt}
                    {res} {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}   {txt}
{txt}June 2020           {res}       -0.842***       -1.183***       -0.930***       -1.314***       -1.119***       -0.600          -1.367***{txt}
                    {res} {ralign 12:{txt:(}0.207{txt:)}}    {ralign 12:{txt:(}0.245{txt:)}}    {ralign 12:{txt:(}0.179{txt:)}}    {ralign 12:{txt:(}0.338{txt:)}}    {ralign 12:{txt:(}0.244{txt:)}}    {ralign 12:{txt:(}0.407{txt:)}}    {ralign 12:{txt:(}0.387{txt:)}}   {txt}
{txt}November 2020       {res}       -0.545**        -0.768***       -0.624***       -0.786**        -0.575**        -0.051          -1.274***{txt}
                    {res} {ralign 12:{txt:(}0.227{txt:)}}    {ralign 12:{txt:(}0.247{txt:)}}    {ralign 12:{txt:(}0.185{txt:)}}    {ralign 12:{txt:(}0.390{txt:)}}    {ralign 12:{txt:(}0.259{txt:)}}    {ralign 12:{txt:(}0.332{txt:)}}    {ralign 12:{txt:(}0.417{txt:)}}   {txt}
{txt}March 2021          {res}       -1.153***       -1.191***       -1.220***       -0.989**        -0.991***       -0.870*         -1.670***{txt}
                    {res} {ralign 12:{txt:(}0.250{txt:)}}    {ralign 12:{txt:(}0.294{txt:)}}    {ralign 12:{txt:(}0.215{txt:)}}    {ralign 12:{txt:(}0.417{txt:)}}    {ralign 12:{txt:(}0.291{txt:)}}    {ralign 12:{txt:(}0.449{txt:)}}    {ralign 12:{txt:(}0.533{txt:)}}   {txt}
{txt}September 2021      {res}       -0.342          -0.889***       -0.631***       -0.478          -0.420          -0.347          -0.753   {txt}
                    {res} {ralign 12:{txt:(}0.275{txt:)}}    {ralign 12:{txt:(}0.292{txt:)}}    {ralign 12:{txt:(}0.228{txt:)}}    {ralign 12:{txt:(}0.410{txt:)}}    {ralign 12:{txt:(}0.303{txt:)}}    {ralign 12:{txt:(}0.484{txt:)}}    {ralign 12:{txt:(}0.502{txt:)}}   {txt}
{txt}November 2022       {res}       -1.224***       -0.997***       -1.053***       -1.392***       -0.873***       -0.700          -1.490***{txt}
                    {res} {ralign 12:{txt:(}0.254{txt:)}}    {ralign 12:{txt:(}0.315{txt:)}}    {ralign 12:{txt:(}0.223{txt:)}}    {ralign 12:{txt:(}0.436{txt:)}}    {ralign 12:{txt:(}0.330{txt:)}}    {ralign 12:{txt:(}0.521{txt:)}}    {ralign 12:{txt:(}0.497{txt:)}}   {txt}
{txt}January 2024        {res}       -0.848***       -1.220***       -0.975***       -1.187***       -1.004***       -0.970**        -1.519***{txt}
                    {res} {ralign 12:{txt:(}0.238{txt:)}}    {ralign 12:{txt:(}0.313{txt:)}}    {ralign 12:{txt:(}0.218{txt:)}}    {ralign 12:{txt:(}0.414{txt:)}}    {ralign 12:{txt:(}0.286{txt:)}}    {ralign 12:{txt:(}0.417{txt:)}}    {ralign 12:{txt:(}0.542{txt:)}}   {txt}
{txt}threatcovid=0 # ~722{res}        0.000           0.000           0.000           0.000           0.000           0.000           0.000   {txt}
                    {res} {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}   {txt}
{txt}threatcovid=1 # ~725{res}       -0.686*         -1.040**        -1.113***        0.292          -0.246          -2.055***       -0.857   {txt}
                    {res} {ralign 12:{txt:(}0.378{txt:)}}    {ralign 12:{txt:(}0.466{txt:)}}    {ralign 12:{txt:(}0.326{txt:)}}    {ralign 12:{txt:(}0.689{txt:)}}    {ralign 12:{txt:(}0.444{txt:)}}    {ralign 12:{txt:(}0.680{txt:)}}    {ralign 12:{txt:(}0.793{txt:)}}   {txt}
{txt}threatcovid=1 # ~730{res}       -1.192***       -1.524***       -1.499***       -0.678          -0.850**        -2.418***       -1.274   {txt}
                    {res} {ralign 12:{txt:(}0.416{txt:)}}    {ralign 12:{txt:(}0.431{txt:)}}    {ralign 12:{txt:(}0.343{txt:)}}    {ralign 12:{txt:(}0.619{txt:)}}    {ralign 12:{txt:(}0.416{txt:)}}    {ralign 12:{txt:(}0.780{txt:)}}    {ralign 12:{txt:(}0.832{txt:)}}   {txt}
{txt}threatcovid=1 # ~734{res}       -1.150**        -1.124**        -1.242***       -0.578          -0.954**        -2.114***       -0.678   {txt}
                    {res} {ralign 12:{txt:(}0.453{txt:)}}    {ralign 12:{txt:(}0.474{txt:)}}    {ralign 12:{txt:(}0.360{txt:)}}    {ralign 12:{txt:(}0.763{txt:)}}    {ralign 12:{txt:(}0.472{txt:)}}    {ralign 12:{txt:(}0.772{txt:)}}    {ralign 12:{txt:(}0.837{txt:)}}   {txt}
{txt}threatcovid=1 # ~740{res}       -2.549***       -2.062***       -2.435***       -1.805**        -2.076***       -3.503***       -3.402***{txt}
                    {res} {ralign 12:{txt:(}0.507{txt:)}}    {ralign 12:{txt:(}0.525{txt:)}}    {ralign 12:{txt:(}0.415{txt:)}}    {ralign 12:{txt:(}0.767{txt:)}}    {ralign 12:{txt:(}0.529{txt:)}}    {ralign 12:{txt:(}0.950{txt:)}}    {ralign 12:{txt:(}0.984{txt:)}}   {txt}
{txt}threatcovid=1 # ~754{res}       -2.829***       -3.074***       -3.217***       -1.927***       -3.120***       -3.962***       -2.893***{txt}
                    {res} {ralign 12:{txt:(}0.442{txt:)}}    {ralign 12:{txt:(}0.511{txt:)}}    {ralign 12:{txt:(}0.382{txt:)}}    {ralign 12:{txt:(}0.709{txt:)}}    {ralign 12:{txt:(}0.504{txt:)}}    {ralign 12:{txt:(}0.792{txt:)}}    {ralign 12:{txt:(}0.889{txt:)}}   {txt}
{txt}threatcovid=1 # ~768{res}       -3.131***       -2.486***       -3.056***       -1.975***       -2.116***       -2.563***       -3.269***{txt}
                    {res} {ralign 12:{txt:(}0.463{txt:)}}    {ralign 12:{txt:(}0.527{txt:)}}    {ralign 12:{txt:(}0.395{txt:)}}    {ralign 12:{txt:(}0.747{txt:)}}    {ralign 12:{txt:(}0.505{txt:)}}    {ralign 12:{txt:(}0.786{txt:)}}    {ralign 12:{txt:(}0.886{txt:)}}   {txt}
{txt}{hline 132}
{txt}Individual FE       {res}          Yes             Yes             Yes             Yes             Yes             Yes             Yes   {txt}
{txt}N. of observations  {res}         1980            1773            3000             753            1610             608             700   {txt}
{txt}N. of unique respo~s{res}          418             353             613             158             325             125             141   {txt}
{txt}R$^2$               {res}        0.495           0.475           0.485           0.479           0.479           0.510           0.452   {txt}
{txt}{hline 132}
{txt}Standard errors clustered by individual in parentheses. *p$<$.1; **p$<$.05; ***p$<$.01.

{com}. 
. 
.   
. ******************************************************************************************************
. *Table F12: Regression estimates for heterogeneity analyses (full models for top panels in Figure E4)*
. ******************************************************************************************************
. 
. quietly {c -(}
{txt}
{com}. 
. esttab hssa*, replace nonumbers nogaps noomit label noobs nonotes ///
>   order(_cons) coeflabels(_cons "Intercept" 722.datewave "March 2020" 725.datewave "June 2020" 730.datewave "November 2020" 734.datewave "March 2021" 740.datewave "September 2021" 754.datewave "November 2022" 768.datewave "January 2024" 1.threatcovid "Threat (Covid)") ///
>   b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
>   collabels("") scalars("ife Individual FE" "n N. of observations" "unique N. of unique respondents" "r2 R$^2$") ///
>   mgroups("Gender" "Age" "Education", pattern(1 0 1 0 1 0 0) span) ///
>   mlabels("Men" "Women" "Under-35" "Over-35" "Primary or less" "Secondary" "Tertiary") ///
>   addnotes("Standard errors clustered by individual in parentheses. *p$<$.1; **p$<$.05; ***p$<$.01.")
{res}
{txt}{hline 132}
{txt}                     Gender                          Age                             Education                                      
{txt}                              Men           Women        Under-35         Over-35    Primary or~s       Secondary        Tertiary   
{txt}                                                                                                                                    
{txt}{hline 132}
{txt}Intercept           {res}        7.238***        7.079***        6.987***        7.201***        7.137***        7.012***        7.289***{txt}
                    {res} {ralign 12:{txt:(}0.129{txt:)}}    {ralign 12:{txt:(}0.123{txt:)}}    {ralign 12:{txt:(}0.185{txt:)}}    {ralign 12:{txt:(}0.102{txt:)}}    {ralign 12:{txt:(}0.145{txt:)}}    {ralign 12:{txt:(}0.160{txt:)}}    {ralign 12:{txt:(}0.160{txt:)}}   {txt}
{txt}threatcovid=0       {res}        0.000           0.000           0.000           0.000           0.000           0.000           0.000   {txt}
                    {res} {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}   {txt}
{txt}Threat (Covid)      {res}        0.591**         0.870***        0.544           0.790***        0.928***        0.886**         0.362   {txt}
                    {res} {ralign 12:{txt:(}0.259{txt:)}}    {ralign 12:{txt:(}0.221{txt:)}}    {ralign 12:{txt:(}0.359{txt:)}}    {ralign 12:{txt:(}0.196{txt:)}}    {ralign 12:{txt:(}0.267{txt:)}}    {ralign 12:{txt:(}0.345{txt:)}}    {ralign 12:{txt:(}0.304{txt:)}}   {txt}
{txt}March 2020          {res}        0.000           0.000           0.000           0.000           0.000           0.000           0.000   {txt}
                    {res} {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}   {txt}
{txt}June 2020           {res}       -0.471**        -0.406**        -0.603**        -0.388***       -0.146          -0.608**        -0.698***{txt}
                    {res} {ralign 12:{txt:(}0.185{txt:)}}    {ralign 12:{txt:(}0.170{txt:)}}    {ralign 12:{txt:(}0.253{txt:)}}    {ralign 12:{txt:(}0.145{txt:)}}    {ralign 12:{txt:(}0.195{txt:)}}    {ralign 12:{txt:(}0.265{txt:)}}    {ralign 12:{txt:(}0.206{txt:)}}   {txt}
{txt}November 2020       {res}       -0.532***       -0.073          -0.770***       -0.194          -0.210           0.014          -0.704***{txt}
                    {res} {ralign 12:{txt:(}0.205{txt:)}}    {ralign 12:{txt:(}0.191{txt:)}}    {ralign 12:{txt:(}0.293{txt:)}}    {ralign 12:{txt:(}0.160{txt:)}}    {ralign 12:{txt:(}0.227{txt:)}}    {ralign 12:{txt:(}0.261{txt:)}}    {ralign 12:{txt:(}0.244{txt:)}}   {txt}
{txt}March 2021          {res}       -1.058***       -0.706***       -0.894***       -0.891***       -0.547**        -0.801**        -1.460***{txt}
                    {res} {ralign 12:{txt:(}0.221{txt:)}}    {ralign 12:{txt:(}0.235{txt:)}}    {ralign 12:{txt:(}0.323{txt:)}}    {ralign 12:{txt:(}0.185{txt:)}}    {ralign 12:{txt:(}0.225{txt:)}}    {ralign 12:{txt:(}0.347{txt:)}}    {ralign 12:{txt:(}0.296{txt:)}}   {txt}
{txt}September 2021      {res}       -1.108***       -0.233          -0.646*         -0.728***       -0.490*         -0.765**        -0.969***{txt}
                    {res} {ralign 12:{txt:(}0.230{txt:)}}    {ralign 12:{txt:(}0.224{txt:)}}    {ralign 12:{txt:(}0.390{txt:)}}    {ralign 12:{txt:(}0.181{txt:)}}    {ralign 12:{txt:(}0.258{txt:)}}    {ralign 12:{txt:(}0.346{txt:)}}    {ralign 12:{txt:(}0.275{txt:)}}   {txt}
{txt}November 2022       {res}       -1.136***       -0.617***       -1.161***       -0.843***       -0.633***       -1.044***       -1.172***{txt}
                    {res} {ralign 12:{txt:(}0.246{txt:)}}    {ralign 12:{txt:(}0.225{txt:)}}    {ralign 12:{txt:(}0.394{txt:)}}    {ralign 12:{txt:(}0.189{txt:)}}    {ralign 12:{txt:(}0.239{txt:)}}    {ralign 12:{txt:(}0.353{txt:)}}    {ralign 12:{txt:(}0.342{txt:)}}   {txt}
{txt}January 2024        {res}       -0.699***       -0.003          -0.177          -0.416**         0.007          -0.624*         -0.812***{txt}
                    {res} {ralign 12:{txt:(}0.225{txt:)}}    {ralign 12:{txt:(}0.228{txt:)}}    {ralign 12:{txt:(}0.415{txt:)}}    {ralign 12:{txt:(}0.174{txt:)}}    {ralign 12:{txt:(}0.229{txt:)}}    {ralign 12:{txt:(}0.337{txt:)}}    {ralign 12:{txt:(}0.299{txt:)}}   {txt}
{txt}threatcovid=0 # ~722{res}        0.000           0.000           0.000           0.000           0.000           0.000           0.000   {txt}
                    {res} {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}   {txt}
{txt}threatcovid=1 # ~725{res}       -0.007           0.207           0.561          -0.055          -0.298          -0.008           0.677*  {txt}
                    {res} {ralign 12:{txt:(}0.356{txt:)}}    {ralign 12:{txt:(}0.323{txt:)}}    {ralign 12:{txt:(}0.491{txt:)}}    {ralign 12:{txt:(}0.277{txt:)}}    {ralign 12:{txt:(}0.375{txt:)}}    {ralign 12:{txt:(}0.535{txt:)}}    {ralign 12:{txt:(}0.381{txt:)}}   {txt}
{txt}threatcovid=1 # ~730{res}        0.063          -0.564*          0.070          -0.304          -0.360          -1.130**         0.696   {txt}
                    {res} {ralign 12:{txt:(}0.388{txt:)}}    {ralign 12:{txt:(}0.327{txt:)}}    {ralign 12:{txt:(}0.483{txt:)}}    {ralign 12:{txt:(}0.299{txt:)}}    {ralign 12:{txt:(}0.388{txt:)}}    {ralign 12:{txt:(}0.509{txt:)}}    {ralign 12:{txt:(}0.461{txt:)}}   {txt}
{txt}threatcovid=1 # ~734{res}       -0.153          -0.161           0.132          -0.244          -0.790*         -0.280           0.771*  {txt}
                    {res} {ralign 12:{txt:(}0.398{txt:)}}    {ralign 12:{txt:(}0.371{txt:)}}    {ralign 12:{txt:(}0.557{txt:)}}    {ralign 12:{txt:(}0.315{txt:)}}    {ralign 12:{txt:(}0.405{txt:)}}    {ralign 12:{txt:(}0.636{txt:)}}    {ralign 12:{txt:(}0.453{txt:)}}   {txt}
{txt}threatcovid=1 # ~740{res}       -0.224          -1.276***       -1.027          -0.619*         -1.096**        -0.401          -0.323   {txt}
                    {res} {ralign 12:{txt:(}0.413{txt:)}}    {ralign 12:{txt:(}0.433{txt:)}}    {ralign 12:{txt:(}0.726{txt:)}}    {ralign 12:{txt:(}0.323{txt:)}}    {ralign 12:{txt:(}0.461{txt:)}}    {ralign 12:{txt:(}0.706{txt:)}}    {ralign 12:{txt:(}0.454{txt:)}}   {txt}
{txt}threatcovid=1 # ~754{res}       -0.946**        -1.342***       -0.406          -1.294***       -1.681***       -0.801          -0.628   {txt}
                    {res} {ralign 12:{txt:(}0.416{txt:)}}    {ralign 12:{txt:(}0.386{txt:)}}    {ralign 12:{txt:(}0.611{txt:)}}    {ralign 12:{txt:(}0.321{txt:)}}    {ralign 12:{txt:(}0.438{txt:)}}    {ralign 12:{txt:(}0.607{txt:)}}    {ralign 12:{txt:(}0.482{txt:)}}   {txt}
{txt}threatcovid=1 # ~768{res}       -1.415***       -1.735***       -1.507**        -1.581***       -2.077***       -1.414**        -0.926*  {txt}
                    {res} {ralign 12:{txt:(}0.425{txt:)}}    {ralign 12:{txt:(}0.369{txt:)}}    {ralign 12:{txt:(}0.751{txt:)}}    {ralign 12:{txt:(}0.305{txt:)}}    {ralign 12:{txt:(}0.417{txt:)}}    {ralign 12:{txt:(}0.550{txt:)}}    {ralign 12:{txt:(}0.538{txt:)}}   {txt}
{txt}{hline 132}
{txt}Individual FE       {res}          Yes             Yes             Yes             Yes             Yes             Yes             Yes   {txt}
{txt}N. of observations  {res}         1994            1759             784            2969            1683             935            1135   {txt}
{txt}N. of unique respo~s{res}          390             381             175             596             339             201             231   {txt}
{txt}R$^2$               {res}        0.507           0.508           0.507           0.507           0.511           0.490           0.522   {txt}
{txt}{hline 132}
{txt}Standard errors clustered by individual in parentheses. *p$<$.1; **p$<$.05; ***p$<$.01.

{com}. 
. 
.   
. *********************************************************************************************************
. *Table F13: Regression estimates for heterogeneity analyses (full models for bottom panels in Figure E4)*
. *********************************************************************************************************
. 
. quietly {c -(}
{txt}
{com}. 
. esttab hssb*, replace nonumbers nogaps noomit label noobs nonotes ///
>   order(_cons) coeflabels(_cons "Intercept" 722.datewave "March 2020" 725.datewave "June 2020" 730.datewave "November 2020" 734.datewave "March 2021" 740.datewave "September 2021" 754.datewave "November 2022" 768.datewave "January 2024") ///
>   b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
>   collabels("") scalars("ife Individual FE" "n N. of observations" "unique N. of unique respondents" "r2 R$^2$") ///
>   mgroups("Political interest" "Incumbent support" "Ideology", pattern(1 0 1 0 1 0 0) span) ///
>   mlabels("Low" "High" "No" "Yes" "Left" "Center" "Right") ///
>   addnotes("Standard errors clustered by individual in parentheses. *p$<$.1; **p$<$.05; ***p$<$.01.")
{res}
{txt}{hline 132}
{txt}                     Political interest              Incumbent support               Ideology                                       
{txt}                              Low            High              No             Yes            Left          Center           Right   
{txt}                                                                                                                                    
{txt}{hline 132}
{txt}Intercept           {res}        7.040***        7.299***        7.136***        7.199***        6.775***        7.394***        8.102***{txt}
                    {res} {ralign 12:{txt:(}0.118{txt:)}}    {ralign 12:{txt:(}0.138{txt:)}}    {ralign 12:{txt:(}0.101{txt:)}}    {ralign 12:{txt:(}0.185{txt:)}}    {ralign 12:{txt:(}0.146{txt:)}}    {ralign 12:{txt:(}0.190{txt:)}}    {ralign 12:{txt:(}0.199{txt:)}}   {txt}
{txt}threatcovid=0       {res}        0.000           0.000           0.000           0.000           0.000           0.000           0.000   {txt}
                    {res} {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}   {txt}
{txt}threatcovid=1       {res}        0.789***        0.656**         0.540***        1.525***        1.173***        0.618           0.353   {txt}
                    {res} {ralign 12:{txt:(}0.228{txt:)}}    {ralign 12:{txt:(}0.263{txt:)}}    {ralign 12:{txt:(}0.199{txt:)}}    {ralign 12:{txt:(}0.325{txt:)}}    {ralign 12:{txt:(}0.245{txt:)}}    {ralign 12:{txt:(}0.388{txt:)}}    {ralign 12:{txt:(}0.408{txt:)}}   {txt}
{txt}March 2020          {res}        0.000           0.000           0.000           0.000           0.000           0.000           0.000   {txt}
                    {res} {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}   {txt}
{txt}June 2020           {res}       -0.235          -0.668***       -0.571***        0.153          -0.215          -0.026          -1.395***{txt}
                    {res} {ralign 12:{txt:(}0.170{txt:)}}    {ralign 12:{txt:(}0.186{txt:)}}    {ralign 12:{txt:(}0.144{txt:)}}    {ralign 12:{txt:(}0.245{txt:)}}    {ralign 12:{txt:(}0.196{txt:)}}    {ralign 12:{txt:(}0.281{txt:)}}    {ralign 12:{txt:(}0.292{txt:)}}   {txt}
{txt}November 2020       {res}       -0.158          -0.494**        -0.377**        -0.024          -0.156          -0.443          -0.857** {txt}
                    {res} {ralign 12:{txt:(}0.180{txt:)}}    {ralign 12:{txt:(}0.220{txt:)}}    {ralign 12:{txt:(}0.158{txt:)}}    {ralign 12:{txt:(}0.309{txt:)}}    {ralign 12:{txt:(}0.229{txt:)}}    {ralign 12:{txt:(}0.297{txt:)}}    {ralign 12:{txt:(}0.336{txt:)}}   {txt}
{txt}March 2021          {res}       -0.725***       -1.084***       -1.040***       -0.270          -0.478**        -0.888***       -1.879***{txt}
                    {res} {ralign 12:{txt:(}0.217{txt:)}}    {ralign 12:{txt:(}0.238{txt:)}}    {ralign 12:{txt:(}0.182{txt:)}}    {ralign 12:{txt:(}0.337{txt:)}}    {ralign 12:{txt:(}0.243{txt:)}}    {ralign 12:{txt:(}0.303{txt:)}}    {ralign 12:{txt:(}0.451{txt:)}}   {txt}
{txt}September 2021      {res}       -0.488**        -0.971***       -0.922***        0.149          -0.452*         -1.118***       -1.106***{txt}
                    {res} {ralign 12:{txt:(}0.225{txt:)}}    {ralign 12:{txt:(}0.241{txt:)}}    {ralign 12:{txt:(}0.187{txt:)}}    {ralign 12:{txt:(}0.323{txt:)}}    {ralign 12:{txt:(}0.270{txt:)}}    {ralign 12:{txt:(}0.369{txt:)}}    {ralign 12:{txt:(}0.375{txt:)}}   {txt}
{txt}November 2022       {res}       -0.764***       -1.061***       -1.038***       -0.307          -0.371          -1.036***       -1.522***{txt}
                    {res} {ralign 12:{txt:(}0.214{txt:)}}    {ralign 12:{txt:(}0.272{txt:)}}    {ralign 12:{txt:(}0.195{txt:)}}    {ralign 12:{txt:(}0.326{txt:)}}    {ralign 12:{txt:(}0.234{txt:)}}    {ralign 12:{txt:(}0.383{txt:)}}    {ralign 12:{txt:(}0.460{txt:)}}   {txt}
{txt}January 2024        {res}        0.009          -0.831***       -0.527***        0.225          -0.097          -0.497          -1.222***{txt}
                    {res} {ralign 12:{txt:(}0.206{txt:)}}    {ralign 12:{txt:(}0.246{txt:)}}    {ralign 12:{txt:(}0.187{txt:)}}    {ralign 12:{txt:(}0.287{txt:)}}    {ralign 12:{txt:(}0.247{txt:)}}    {ralign 12:{txt:(}0.324{txt:)}}    {ralign 12:{txt:(}0.393{txt:)}}   {txt}
{txt}threatcovid=0 # ~722{res}        0.000           0.000           0.000           0.000           0.000           0.000           0.000   {txt}
                    {res} {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}   {txt}
{txt}threatcovid=1 # ~725{res}       -0.072           0.262           0.322          -0.916*          0.069          -0.657           0.836   {txt}
                    {res} {ralign 12:{txt:(}0.312{txt:)}}    {ralign 12:{txt:(}0.379{txt:)}}    {ralign 12:{txt:(}0.272{txt:)}}    {ralign 12:{txt:(}0.521{txt:)}}    {ralign 12:{txt:(}0.366{txt:)}}    {ralign 12:{txt:(}0.594{txt:)}}    {ralign 12:{txt:(}0.540{txt:)}}   {txt}
{txt}threatcovid=1 # ~730{res}       -0.349          -0.114          -0.086          -0.887*         -0.534          -0.101           0.426   {txt}
                    {res} {ralign 12:{txt:(}0.340{txt:)}}    {ralign 12:{txt:(}0.384{txt:)}}    {ralign 12:{txt:(}0.291{txt:)}}    {ralign 12:{txt:(}0.525{txt:)}}    {ralign 12:{txt:(}0.388{txt:)}}    {ralign 12:{txt:(}0.659{txt:)}}    {ralign 12:{txt:(}0.633{txt:)}}   {txt}
{txt}threatcovid=1 # ~734{res}       -0.311          -0.007           0.124          -1.361**        -0.489          -0.338           0.689   {txt}
                    {res} {ralign 12:{txt:(}0.392{txt:)}}    {ralign 12:{txt:(}0.385{txt:)}}    {ralign 12:{txt:(}0.303{txt:)}}    {ralign 12:{txt:(}0.642{txt:)}}    {ralign 12:{txt:(}0.396{txt:)}}    {ralign 12:{txt:(}0.549{txt:)}}    {ralign 12:{txt:(}0.698{txt:)}}   {txt}
{txt}threatcovid=1 # ~740{res}       -1.058**        -0.317          -0.400          -1.861***       -1.133***       -0.143          -0.623   {txt}
                    {res} {ralign 12:{txt:(}0.439{txt:)}}    {ralign 12:{txt:(}0.404{txt:)}}    {ralign 12:{txt:(}0.339{txt:)}}    {ralign 12:{txt:(}0.601{txt:)}}    {ralign 12:{txt:(}0.421{txt:)}}    {ralign 12:{txt:(}0.795{txt:)}}    {ralign 12:{txt:(}0.805{txt:)}}   {txt}
{txt}threatcovid=1 # ~754{res}       -1.160***       -1.073**        -1.032***       -1.683***       -1.966***       -0.943           0.197   {txt}
                    {res} {ralign 12:{txt:(}0.376{txt:)}}    {ralign 12:{txt:(}0.439{txt:)}}    {ralign 12:{txt:(}0.324{txt:)}}    {ralign 12:{txt:(}0.588{txt:)}}    {ralign 12:{txt:(}0.399{txt:)}}    {ralign 12:{txt:(}0.698{txt:)}}    {ralign 12:{txt:(}0.687{txt:)}}   {txt}
{txt}threatcovid=1 # ~768{res}       -2.142***       -0.878**        -1.260***       -2.729***       -1.591***       -1.070          -1.253*  {txt}
                    {res} {ralign 12:{txt:(}0.369{txt:)}}    {ralign 12:{txt:(}0.432{txt:)}}    {ralign 12:{txt:(}0.331{txt:)}}    {ralign 12:{txt:(}0.500{txt:)}}    {ralign 12:{txt:(}0.404{txt:)}}    {ralign 12:{txt:(}0.653{txt:)}}    {ralign 12:{txt:(}0.643{txt:)}}   {txt}
{txt}{hline 132}
{txt}Individual FE       {res}          Yes             Yes             Yes             Yes             Yes             Yes             Yes   {txt}
{txt}N. of observations  {res}         1980            1773            3000             753            1610             608             700   {txt}
{txt}N. of unique respo~s{res}          418             353             613             158             325             125             141   {txt}
{txt}R$^2$               {res}        0.475           0.536           0.504           0.497           0.534           0.477           0.459   {txt}
{txt}{hline 132}
{txt}Standard errors clustered by individual in parentheses. *p$<$.1; **p$<$.05; ***p$<$.01.

{com}. 
. 
.   
. ******************************************************************************************************
. *Table F14: Regression estimates for heterogeneity analyses (full models for top panels in Figure E5)*
. ******************************************************************************************************
. 
. quietly {c -(}
{txt}
{com}. 
. esttab hlea*, replace nonumbers nogaps noomit label noobs nonotes ///
>   order(_cons) coeflabels(_cons "Intercept" 722.datewave "March 2020" 725.datewave "June 2020" 730.datewave "November 2020" 734.datewave "March 2021" 740.datewave "September 2021" 754.datewave "November 2022" 768.datewave "January 2024") ///
>   b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
>   collabels("") scalars("ife Individual FE" "n N. of observations" "unique N. of unique respondents" "r2 R$^2$") ///
>   mgroups("Health exposure" "Health risk (obj)" "Health risk (subj)", pattern(1 0 1 0 1) span) ///
>   mlabels("Non-infected" "Infected" "Under-60" "Over-60" "") ///
>   addnotes("Standard errors clustered by individual in parentheses. *p$<$.1; **p$<$.05; ***p$<$.01.")
{res}
{txt}{hline 100}
{txt}                     Health exposure                 Health risk (obj)               Health risk (~)
{txt}                     Non-infected        Infected        Under-60         Over-60                   
{txt}                                                                                                    
{txt}{hline 100}
{txt}Intercept           {res}        5.812***        6.161***        5.742***        5.975***        5.681***{txt}
                    {res} {ralign 12:{txt:(}0.089{txt:)}}    {ralign 12:{txt:(}0.581{txt:)}}    {ralign 12:{txt:(}0.123{txt:)}}    {ralign 12:{txt:(}0.271{txt:)}}    {ralign 12:{txt:(}0.216{txt:)}}   {txt}
{txt}threatcovid=0       {res}        0.000           0.000           0.000           0.000           0.000   {txt}
                    {res} {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}   {txt}
{txt}threatcovid=1       {res}        2.551***        1.358           2.722***        2.848***        2.360***{txt}
                    {res} {ralign 12:{txt:(}0.173{txt:)}}    {ralign 12:{txt:(}0.994{txt:)}}    {ralign 12:{txt:(}0.247{txt:)}}    {ralign 12:{txt:(}0.431{txt:)}}    {ralign 12:{txt:(}0.406{txt:)}}   {txt}
{txt}March 2020          {res}        0.000           0.000           0.000           0.000           0.000   {txt}
                    {res} {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}   {txt}
{txt}June 2020           {res}       -0.975***       -1.975***       -1.134***       -0.440          -0.611** {txt}
                    {res} {ralign 12:{txt:(}0.126{txt:)}}    {ralign 12:{txt:(}0.650{txt:)}}    {ralign 12:{txt:(}0.177{txt:)}}    {ralign 12:{txt:(}0.359{txt:)}}    {ralign 12:{txt:(}0.301{txt:)}}   {txt}
{txt}November 2020       {res}       -0.891***       -0.876          -0.774***       -0.169          -0.830***{txt}
                    {res} {ralign 12:{txt:(}0.128{txt:)}}    {ralign 12:{txt:(}0.589{txt:)}}    {ralign 12:{txt:(}0.185{txt:)}}    {ralign 12:{txt:(}0.389{txt:)}}    {ralign 12:{txt:(}0.303{txt:)}}   {txt}
{txt}March 2021          {res}       -1.078***       -1.355**        -1.243***       -0.858*         -1.107***{txt}
                    {res} {ralign 12:{txt:(}0.131{txt:)}}    {ralign 12:{txt:(}0.657{txt:)}}    {ralign 12:{txt:(}0.208{txt:)}}    {ralign 12:{txt:(}0.468{txt:)}}    {ralign 12:{txt:(}0.299{txt:)}}   {txt}
{txt}September 2021      {res}       -0.486***       -1.373**        -0.541**        -0.746          -0.817** {txt}
                    {res} {ralign 12:{txt:(}0.158{txt:)}}    {ralign 12:{txt:(}0.650{txt:)}}    {ralign 12:{txt:(}0.223{txt:)}}    {ralign 12:{txt:(}0.451{txt:)}}    {ralign 12:{txt:(}0.338{txt:)}}   {txt}
{txt}November 2022       {res}       -0.956***       -0.890          -1.127***       -0.965**        -0.531*  {txt}
                    {res} {ralign 12:{txt:(}0.180{txt:)}}    {ralign 12:{txt:(}0.614{txt:)}}    {ralign 12:{txt:(}0.229{txt:)}}    {ralign 12:{txt:(}0.415{txt:)}}    {ralign 12:{txt:(}0.309{txt:)}}   {txt}
{txt}January 2024        {res}       -0.744***       -1.350**        -1.142***       -0.556          -0.762** {txt}
                    {res} {ralign 12:{txt:(}0.188{txt:)}}    {ralign 12:{txt:(}0.610{txt:)}}    {ralign 12:{txt:(}0.216{txt:)}}    {ralign 12:{txt:(}0.436{txt:)}}    {ralign 12:{txt:(}0.308{txt:)}}   {txt}
{txt}threatcovid=0 # ~722{res}        0.000           0.000           0.000           0.000           0.000   {txt}
                    {res} {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}   {txt}
{txt}threatcovid=1 # ~725{res}       -0.930***        1.319          -0.747**        -1.247**        -1.484***{txt}
                    {res} {ralign 12:{txt:(}0.234{txt:)}}    {ralign 12:{txt:(}1.190{txt:)}}    {ralign 12:{txt:(}0.343{txt:)}}    {ralign 12:{txt:(}0.552{txt:)}}    {ralign 12:{txt:(}0.543{txt:)}}   {txt}
{txt}threatcovid=1 # ~730{res}       -0.805***       -0.046          -1.252***       -1.687**        -1.178** {txt}
                    {res} {ralign 12:{txt:(}0.222{txt:)}}    {ralign 12:{txt:(}1.095{txt:)}}    {ralign 12:{txt:(}0.337{txt:)}}    {ralign 12:{txt:(}0.654{txt:)}}    {ralign 12:{txt:(}0.572{txt:)}}   {txt}
{txt}threatcovid=1 # ~734{res}       -1.266***        0.268          -1.040***       -1.427**        -0.941   {txt}
                    {res} {ralign 12:{txt:(}0.244{txt:)}}    {ralign 12:{txt:(}1.101{txt:)}}    {ralign 12:{txt:(}0.372{txt:)}}    {ralign 12:{txt:(}0.673{txt:)}}    {ralign 12:{txt:(}0.574{txt:)}}   {txt}
{txt}threatcovid=1 # ~740{res}       -2.440***       -1.005          -2.254***       -2.536***       -1.970***{txt}
                    {res} {ralign 12:{txt:(}0.267{txt:)}}    {ralign 12:{txt:(}1.199{txt:)}}    {ralign 12:{txt:(}0.403{txt:)}}    {ralign 12:{txt:(}0.850{txt:)}}    {ralign 12:{txt:(}0.602{txt:)}}   {txt}
{txt}threatcovid=1 # ~754{res}       -2.955***       -2.074**        -2.980***       -2.909***       -3.372***{txt}
                    {res} {ralign 12:{txt:(}0.302{txt:)}}    {ralign 12:{txt:(}1.034{txt:)}}    {ralign 12:{txt:(}0.389{txt:)}}    {ralign 12:{txt:(}0.672{txt:)}}    {ralign 12:{txt:(}0.534{txt:)}}   {txt}
{txt}threatcovid=1 # ~768{res}       -2.911***       -1.736*         -2.919***       -2.741***       -2.999***{txt}
                    {res} {ralign 12:{txt:(}0.320{txt:)}}    {ralign 12:{txt:(}1.025{txt:)}}    {ralign 12:{txt:(}0.408{txt:)}}    {ralign 12:{txt:(}0.683{txt:)}}    {ralign 12:{txt:(}0.556{txt:)}}   {txt}
{txt}subjserious         {res}                                                                        0.036   {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.043{txt:)}}   {txt}
{txt}threatcovid=1 # su~s{res}                                                                        0.028   {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.076{txt:)}}   {txt}
{txt}datewave=725 # sub~s{res}                                                                       -0.058   {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.057{txt:)}}   {txt}
{txt}datewave=730 # sub~s{res}                                                                        0.009   {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.056{txt:)}}   {txt}
{txt}datewave=734 # sub~s{res}                                                                        0.014   {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.056{txt:)}}   {txt}
{txt}datewave=740 # sub~s{res}                                                                        0.044   {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.065{txt:)}}   {txt}
{txt}datewave=754 # sub~s{res}                                                                       -0.056   {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.061{txt:)}}   {txt}
{txt}datewave=768 # sub~s{res}                                                                       -0.049   {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.063{txt:)}}   {txt}
{txt}threatcovid=1 # da~s{res}                                                                        0.131   {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.104{txt:)}}   {txt}
{txt}threatcovid=1 # da~s{res}                                                                        0.058   {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.106{txt:)}}   {txt}
{txt}threatcovid=1 # da~s{res}                                                                       -0.034   {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.109{txt:)}}   {txt}
{txt}threatcovid=1 # da~s{res}                                                                       -0.023   {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.114{txt:)}}   {txt}
{txt}threatcovid=1 # da~s{res}                                                                        0.113   {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.106{txt:)}}   {txt}
{txt}threatcovid=1 # da~s{res}                                                                        0.076   {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.111{txt:)}}   {txt}
{txt}{hline 100}
{txt}Individual FE       {res}          Yes             Yes             Yes             Yes             Yes   {txt}
{txt}N. of observations  {res}         6518            1885            2929             824            7333   {txt}
{txt}N. of unique respo~s{res}         1724             761             611             160            1914   {txt}
{txt}R$^2$               {res}        0.533           0.594           0.477           0.505           0.517   {txt}
{txt}{hline 100}
{txt}Standard errors clustered by individual in parentheses. *p$<$.1; **p$<$.05; ***p$<$.01.

{com}. 
. 
.   
. *********************************************************************************************************
. *Table F15: Regression estimates for heterogeneity analyses (full models for bottom panels in Figure E5)*
. *********************************************************************************************************
. 
. quietly {c -(}
{txt}
{com}. 
. esttab hleb*, replace nonumbers nogaps noomit label noobs nonotes ///
>   order(_cons) coeflabels(_cons "Intercept" 722.datewave "March 2020" 725.datewave "June 2020" 730.datewave "November 2020" 734.datewave "March 2021" 740.datewave "September 2021" 754.datewave "November 2022" 768.datewave "January 2024") ///
>   b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
>   collabels("") scalars("ife Individual FE" "n N. of observations" "unique N. of unique respondents" "r2 R$^2$") ///
>   mgroups("Economic risk (subj)" "Health risk (pred)" "Economic risk (pred)", pattern(1 0 1 1) span) ///
>   mlabels("Low" "High" "" "") ///
>   addnotes("Standard errors clustered by individual in parentheses. *p$<$.1; **p$<$.05; ***p$<$.01.")
{res}
{txt}{hline 84}
{txt}                     Economic risk (subj)            Health risk (~) Economic risk~)
{txt}                              Low            High                                   
{txt}                                                                                    
{txt}{hline 84}
{txt}Intercept           {res}        6.027***        5.853***        5.745***        6.138***{txt}
                    {res} {ralign 12:{txt:(}0.239{txt:)}}    {ralign 12:{txt:(}0.100{txt:)}}    {ralign 12:{txt:(}0.639{txt:)}}    {ralign 12:{txt:(}0.956{txt:)}}   {txt}
{txt}threatcovid=0       {res}        0.000           0.000           0.000           0.000   {txt}
                    {res} {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}   {txt}
{txt}threatcovid=1       {res}        1.727***        2.482***        2.944***        2.813*  {txt}
                    {res} {ralign 12:{txt:(}0.425{txt:)}}    {ralign 12:{txt:(}0.203{txt:)}}    {ralign 12:{txt:(}0.989{txt:)}}    {ralign 12:{txt:(}1.530{txt:)}}   {txt}
{txt}March 2020          {res}        0.000           0.000           0.000           0.000   {txt}
                    {res} {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}   {txt}
{txt}June 2020           {res}       -1.190***       -1.165***       -0.395          -0.546   {txt}
                    {res} {ralign 12:{txt:(}0.301{txt:)}}    {ralign 12:{txt:(}0.140{txt:)}}    {ralign 12:{txt:(}0.698{txt:)}}    {ralign 12:{txt:(}1.244{txt:)}}   {txt}
{txt}November 2020       {res}       -0.812***       -0.897***       -0.902          -2.015   {txt}
                    {res} {ralign 12:{txt:(}0.283{txt:)}}    {ralign 12:{txt:(}0.144{txt:)}}    {ralign 12:{txt:(}0.677{txt:)}}    {ralign 12:{txt:(}1.250{txt:)}}   {txt}
{txt}March 2021          {res}       -1.193***       -1.064***       -0.890          -2.087*  {txt}
                    {res} {ralign 12:{txt:(}0.285{txt:)}}    {ralign 12:{txt:(}0.161{txt:)}}    {ralign 12:{txt:(}0.677{txt:)}}    {ralign 12:{txt:(}1.203{txt:)}}   {txt}
{txt}September 2021      {res}       -0.990***       -0.579***        0.160          -0.335   {txt}
                    {res} {ralign 12:{txt:(}0.312{txt:)}}    {ralign 12:{txt:(}0.190{txt:)}}    {ralign 12:{txt:(}0.755{txt:)}}    {ralign 12:{txt:(}1.467{txt:)}}   {txt}
{txt}November 2022       {res}       -0.913***       -0.923***       -0.600          -1.697   {txt}
                    {res} {ralign 12:{txt:(}0.297{txt:)}}    {ralign 12:{txt:(}0.177{txt:)}}    {ralign 12:{txt:(}0.737{txt:)}}    {ralign 12:{txt:(}1.470{txt:)}}   {txt}
{txt}January 2024        {res}       -1.051***       -1.015***       -1.054          -4.266***{txt}
                    {res} {ralign 12:{txt:(}0.286{txt:)}}    {ralign 12:{txt:(}0.181{txt:)}}    {ralign 12:{txt:(}0.739{txt:)}}    {ralign 12:{txt:(}1.468{txt:)}}   {txt}
{txt}threatcovid=0 # ~722{res}        0.000           0.000           0.000           0.000   {txt}
                    {res} {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}   {txt}
{txt}threatcovid=1 # ~725{res}        0.367          -0.955***       -1.298          -3.227   {txt}
                    {res} {ralign 12:{txt:(}0.512{txt:)}}    {ralign 12:{txt:(}0.275{txt:)}}    {ralign 12:{txt:(}1.264{txt:)}}    {ralign 12:{txt:(}2.213{txt:)}}   {txt}
{txt}threatcovid=1 # ~730{res}       -0.145          -0.988***       -1.489          -1.162   {txt}
                    {res} {ralign 12:{txt:(}0.512{txt:)}}    {ralign 12:{txt:(}0.262{txt:)}}    {ralign 12:{txt:(}1.259{txt:)}}    {ralign 12:{txt:(}2.087{txt:)}}   {txt}
{txt}threatcovid=1 # ~734{res}       -0.356          -1.319***       -1.722          -0.276   {txt}
                    {res} {ralign 12:{txt:(}0.486{txt:)}}    {ralign 12:{txt:(}0.308{txt:)}}    {ralign 12:{txt:(}1.286{txt:)}}    {ralign 12:{txt:(}2.279{txt:)}}   {txt}
{txt}threatcovid=1 # ~740{res}       -1.532***       -2.340***       -4.609***       -4.661*  {txt}
                    {res} {ralign 12:{txt:(}0.534{txt:)}}    {ralign 12:{txt:(}0.337{txt:)}}    {ralign 12:{txt:(}1.370{txt:)}}    {ralign 12:{txt:(}2.443{txt:)}}   {txt}
{txt}threatcovid=1 # ~754{res}       -2.268***       -2.800***       -3.741***       -4.314*  {txt}
                    {res} {ralign 12:{txt:(}0.500{txt:)}}    {ralign 12:{txt:(}0.302{txt:)}}    {ralign 12:{txt:(}1.246{txt:)}}    {ralign 12:{txt:(}2.231{txt:)}}   {txt}
{txt}threatcovid=1 # ~768{res}       -2.414***       -2.937***       -4.590***        0.418   {txt}
                    {res} {ralign 12:{txt:(}0.494{txt:)}}    {ralign 12:{txt:(}0.317{txt:)}}    {ralign 12:{txt:(}1.304{txt:)}}    {ralign 12:{txt:(}2.460{txt:)}}   {txt}
{txt}Fitted values       {res}                                        0.016                   {txt}
                    {res}                                 {ralign 12:{txt:(}0.141{txt:)}}                   {txt}
{txt}threatcovid=1 # Fi~s{res}                                       -0.068                   {txt}
                    {res}                                 {ralign 12:{txt:(}0.219{txt:)}}                   {txt}
{txt}datewave=725 # Fit~s{res}                                       -0.117                   {txt}
                    {res}                                 {ralign 12:{txt:(}0.156{txt:)}}                   {txt}
{txt}datewave=730 # Fit~s{res}                                        0.034                   {txt}
                    {res}                                 {ralign 12:{txt:(}0.150{txt:)}}                   {txt}
{txt}datewave=734 # Fit~s{res}                                       -0.010                   {txt}
                    {res}                                 {ralign 12:{txt:(}0.150{txt:)}}                   {txt}
{txt}datewave=740 # Fit~s{res}                                       -0.167                   {txt}
                    {res}                                 {ralign 12:{txt:(}0.165{txt:)}}                   {txt}
{txt}datewave=754 # Fit~s{res}                                       -0.049                   {txt}
                    {res}                                 {ralign 12:{txt:(}0.157{txt:)}}                   {txt}
{txt}datewave=768 # Fit~s{res}                                        0.013                   {txt}
                    {res}                                 {ralign 12:{txt:(}0.161{txt:)}}                   {txt}
{txt}threatcovid=1 # da~F{res}                                        0.108                   {txt}
                    {res}                                 {ralign 12:{txt:(}0.281{txt:)}}                   {txt}
{txt}threatcovid=1 # da~F{res}                                        0.085                   {txt}
                    {res}                                 {ralign 12:{txt:(}0.283{txt:)}}                   {txt}
{txt}threatcovid=1 # da~F{res}                                        0.082                   {txt}
                    {res}                                 {ralign 12:{txt:(}0.286{txt:)}}                   {txt}
{txt}threatcovid=1 # da~F{res}                                        0.504*                  {txt}
                    {res}                                 {ralign 12:{txt:(}0.304{txt:)}}                   {txt}
{txt}threatcovid=1 # da~F{res}                                        0.149                   {txt}
                    {res}                                 {ralign 12:{txt:(}0.271{txt:)}}                   {txt}
{txt}threatcovid=1 # da~F{res}                                        0.364                   {txt}
                    {res}                                 {ralign 12:{txt:(}0.283{txt:)}}                   {txt}
{txt}Fitted values       {res}                                                       -0.136   {txt}
                    {res}                                                 {ralign 12:{txt:(}0.453{txt:)}}   {txt}
{txt}threatcovid=1 # Fi~s{res}                                                       -0.162   {txt}
                    {res}                                                 {ralign 12:{txt:(}0.725{txt:)}}   {txt}
{txt}datewave=725 # Fit~s{res}                                                       -0.234   {txt}
                    {res}                                                 {ralign 12:{txt:(}0.577{txt:)}}   {txt}
{txt}datewave=730 # Fit~s{res}                                                        0.563   {txt}
                    {res}                                                 {ralign 12:{txt:(}0.581{txt:)}}   {txt}
{txt}datewave=734 # Fit~s{res}                                                        0.495   {txt}
                    {res}                                                 {ralign 12:{txt:(}0.561{txt:)}}   {txt}
{txt}datewave=740 # Fit~s{res}                                                       -0.139   {txt}
                    {res}                                                 {ralign 12:{txt:(}0.676{txt:)}}   {txt}
{txt}datewave=754 # Fit~s{res}                                                        0.390   {txt}
                    {res}                                                 {ralign 12:{txt:(}0.675{txt:)}}   {txt}
{txt}datewave=768 # Fit~s{res}                                                        1.480** {txt}
                    {res}                                                 {ralign 12:{txt:(}0.673{txt:)}}   {txt}
{txt}threatcovid=1 # da~F{res}                                                        1.135   {txt}
                    {res}                                                 {ralign 12:{txt:(}1.027{txt:)}}   {txt}
{txt}threatcovid=1 # da~F{res}                                                        0.114   {txt}
                    {res}                                                 {ralign 12:{txt:(}0.971{txt:)}}   {txt}
{txt}threatcovid=1 # da~F{res}                                                       -0.432   {txt}
                    {res}                                                 {ralign 12:{txt:(}1.059{txt:)}}   {txt}
{txt}threatcovid=1 # da~F{res}                                                        1.123   {txt}
                    {res}                                                 {ralign 12:{txt:(}1.138{txt:)}}   {txt}
{txt}threatcovid=1 # da~F{res}                                                        0.650   {txt}
                    {res}                                                 {ralign 12:{txt:(}1.035{txt:)}}   {txt}
{txt}threatcovid=1 # da~F{res}                                                       -1.458   {txt}
                    {res}                                                 {ralign 12:{txt:(}1.129{txt:)}}   {txt}
{txt}{hline 84}
{txt}Individual FE       {res}          Yes             Yes             Yes             Yes   {txt}
{txt}N. of observations  {res}         2814            5352            7627            9041   {txt}
{txt}N. of unique respo~s{res}          884            1501            1758            2177   {txt}
{txt}R$^2$               {res}        0.566           0.542           0.504           0.509   {txt}
{txt}{hline 84}
{txt}Standard errors clustered by individual in parentheses. *p$<$.1; **p$<$.05; ***p$<$.01.

{com}. 
.   
. 
. ******************************************************************************************************
. *Table F16: Regression estimates for heterogeneity analyses (full models for top panels in Figure E6)*
. ******************************************************************************************************
. 
. quietly {c -(}
{txt}
{com}. 
. esttab hsea*, replace nonumbers nogaps noomit label noobs nonotes ///
>   order(_cons) coeflabels(_cons "Intercept" 722.datewave "March 2020" 725.datewave "June 2020" 730.datewave "November 2020" 734.datewave "March 2021" 740.datewave "September 2021" 754.datewave "November 2022" 768.datewave "January 2024") ///
>   b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
>   collabels("") scalars("ife Individual FE" "n N. of observations" "unique N. of unique respondents" "r2 R$^2$") ///
>   mgroups("Health exposure" "Health risk (obj)" "Health risk (subj)", pattern(1 0 1 0 1) span) ///
>   mlabels("Non-infected" "Infected" "Under-60" "Over-60" "") ///
>   addnotes("Standard errors clustered by individual in parentheses. *p$<$.1; **p$<$.05; ***p$<$.01.")
{res}
{txt}{hline 100}
{txt}                     Health exposure                 Health risk (obj)               Health risk (~)
{txt}                     Non-infected        Infected        Under-60         Over-60                   
{txt}                                                                                                    
{txt}{hline 100}
{txt}Intercept           {res}        7.209***        7.091***        7.138***        7.194***        6.917***{txt}
                    {res} {ralign 12:{txt:(}0.073{txt:)}}    {ralign 12:{txt:(}0.357{txt:)}}    {ralign 12:{txt:(}0.099{txt:)}}    {ralign 12:{txt:(}0.207{txt:)}}    {ralign 12:{txt:(}0.175{txt:)}}   {txt}
{txt}threatcovid=0       {res}        0.000           0.000           0.000           0.000           0.000   {txt}
                    {res} {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}   {txt}
{txt}threatcovid=1       {res}        0.562***        1.027*          0.725***        0.791**         0.764***{txt}
                    {res} {ralign 12:{txt:(}0.138{txt:)}}    {ralign 12:{txt:(}0.591{txt:)}}    {ralign 12:{txt:(}0.192{txt:)}}    {ralign 12:{txt:(}0.384{txt:)}}    {ralign 12:{txt:(}0.286{txt:)}}   {txt}
{txt}March 2020          {res}        0.000           0.000           0.000           0.000           0.000   {txt}
                    {res} {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}   {txt}
{txt}June 2020           {res}       -0.504***       -0.378          -0.500***       -0.154           0.049   {txt}
                    {res} {ralign 12:{txt:(}0.104{txt:)}}    {ralign 12:{txt:(}0.420{txt:)}}    {ralign 12:{txt:(}0.142{txt:)}}    {ralign 12:{txt:(}0.268{txt:)}}    {ralign 12:{txt:(}0.225{txt:)}}   {txt}
{txt}November 2020       {res}       -0.442***       -0.428          -0.426***        0.121          -0.302   {txt}
                    {res} {ralign 12:{txt:(}0.110{txt:)}}    {ralign 12:{txt:(}0.407{txt:)}}    {ralign 12:{txt:(}0.157{txt:)}}    {ralign 12:{txt:(}0.315{txt:)}}    {ralign 12:{txt:(}0.258{txt:)}}   {txt}
{txt}March 2021          {res}       -0.985***       -0.957**        -0.879***       -0.918**        -0.667** {txt}
                    {res} {ralign 12:{txt:(}0.117{txt:)}}    {ralign 12:{txt:(}0.446{txt:)}}    {ralign 12:{txt:(}0.176{txt:)}}    {ralign 12:{txt:(}0.384{txt:)}}    {ralign 12:{txt:(}0.267{txt:)}}   {txt}
{txt}September 2021      {res}       -0.732***       -0.667          -0.751***       -0.572          -0.665** {txt}
                    {res} {ralign 12:{txt:(}0.133{txt:)}}    {ralign 12:{txt:(}0.456{txt:)}}    {ralign 12:{txt:(}0.183{txt:)}}    {ralign 12:{txt:(}0.369{txt:)}}    {ralign 12:{txt:(}0.275{txt:)}}   {txt}
{txt}November 2022       {res}       -0.903***       -0.587          -0.959***       -0.662**        -0.512*  {txt}
                    {res} {ralign 12:{txt:(}0.163{txt:)}}    {ralign 12:{txt:(}0.387{txt:)}}    {ralign 12:{txt:(}0.201{txt:)}}    {ralign 12:{txt:(}0.329{txt:)}}    {ralign 12:{txt:(}0.267{txt:)}}   {txt}
{txt}January 2024        {res}       -0.931***       -0.531          -0.549***        0.188          -0.449*  {txt}
                    {res} {ralign 12:{txt:(}0.172{txt:)}}    {ralign 12:{txt:(}0.375{txt:)}}    {ralign 12:{txt:(}0.181{txt:)}}    {ralign 12:{txt:(}0.341{txt:)}}    {ralign 12:{txt:(}0.253{txt:)}}   {txt}
{txt}threatcovid=0 # ~722{res}        0.000           0.000           0.000           0.000           0.000   {txt}
                    {res} {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}   {txt}
{txt}threatcovid=1 # ~725{res}        0.102           0.411           0.203          -0.420          -0.594   {txt}
                    {res} {ralign 12:{txt:(}0.194{txt:)}}    {ralign 12:{txt:(}0.801{txt:)}}    {ralign 12:{txt:(}0.270{txt:)}}    {ralign 12:{txt:(}0.537{txt:)}}    {ralign 12:{txt:(}0.430{txt:)}}   {txt}
{txt}threatcovid=1 # ~730{res}       -0.021          -0.355          -0.204          -0.362          -0.685   {txt}
                    {res} {ralign 12:{txt:(}0.195{txt:)}}    {ralign 12:{txt:(}0.737{txt:)}}    {ralign 12:{txt:(}0.277{txt:)}}    {ralign 12:{txt:(}0.635{txt:)}}    {ralign 12:{txt:(}0.426{txt:)}}   {txt}
{txt}threatcovid=1 # ~734{res}       -0.190           0.137          -0.143          -0.295          -0.600   {txt}
                    {res} {ralign 12:{txt:(}0.208{txt:)}}    {ralign 12:{txt:(}0.666{txt:)}}    {ralign 12:{txt:(}0.313{txt:)}}    {ralign 12:{txt:(}0.563{txt:)}}    {ralign 12:{txt:(}0.458{txt:)}}   {txt}
{txt}threatcovid=1 # ~740{res}       -0.654***       -1.474*         -0.748**        -0.485          -1.031** {txt}
                    {res} {ralign 12:{txt:(}0.228{txt:)}}    {ralign 12:{txt:(}0.760{txt:)}}    {ralign 12:{txt:(}0.327{txt:)}}    {ralign 12:{txt:(}0.696{txt:)}}    {ralign 12:{txt:(}0.461{txt:)}}   {txt}
{txt}threatcovid=1 # ~754{res}       -0.956***       -1.925***       -1.026***       -1.502**        -2.049***{txt}
                    {res} {ralign 12:{txt:(}0.264{txt:)}}    {ralign 12:{txt:(}0.628{txt:)}}    {ralign 12:{txt:(}0.306{txt:)}}    {ralign 12:{txt:(}0.688{txt:)}}    {ralign 12:{txt:(}0.430{txt:)}}   {txt}
{txt}threatcovid=1 # ~768{res}       -1.254***       -1.838***       -1.572***       -1.671***       -1.415***{txt}
                    {res} {ralign 12:{txt:(}0.300{txt:)}}    {ralign 12:{txt:(}0.629{txt:)}}    {ralign 12:{txt:(}0.331{txt:)}}    {ralign 12:{txt:(}0.563{txt:)}}    {ralign 12:{txt:(}0.453{txt:)}}   {txt}
{txt}subjserious         {res}                                                                        0.058*  {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.035{txt:)}}   {txt}
{txt}threatcovid=1 # su~s{res}                                                                       -0.031   {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.058{txt:)}}   {txt}
{txt}datewave=725 # sub~s{res}                                                                       -0.088** {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.044{txt:)}}   {txt}
{txt}datewave=730 # sub~s{res}                                                                       -0.018   {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.047{txt:)}}   {txt}
{txt}datewave=734 # sub~s{res}                                                                       -0.066   {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.048{txt:)}}   {txt}
{txt}datewave=740 # sub~s{res}                                                                       -0.024   {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.054{txt:)}}   {txt}
{txt}datewave=754 # sub~s{res}                                                                       -0.072   {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.055{txt:)}}   {txt}
{txt}datewave=768 # sub~s{res}                                                                       -0.065   {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.052{txt:)}}   {txt}
{txt}threatcovid=1 # da~s{res}                                                                        0.121   {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.080{txt:)}}   {txt}
{txt}threatcovid=1 # da~s{res}                                                                        0.107   {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.081{txt:)}}   {txt}
{txt}threatcovid=1 # da~s{res}                                                                        0.104   {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.085{txt:)}}   {txt}
{txt}threatcovid=1 # da~s{res}                                                                        0.079   {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.091{txt:)}}   {txt}
{txt}threatcovid=1 # da~s{res}                                                                        0.222** {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.090{txt:)}}   {txt}
{txt}threatcovid=1 # da~s{res}                                                                        0.013   {txt}
                    {res}                                                                 {ralign 12:{txt:(}0.096{txt:)}}   {txt}
{txt}{hline 100}
{txt}Individual FE       {res}          Yes             Yes             Yes             Yes             Yes   {txt}
{txt}N. of observations  {res}         6518            1885            2929             824            7333   {txt}
{txt}N. of unique respo~s{res}         1724             761             611             160            1914   {txt}
{txt}R$^2$               {res}        0.543           0.641           0.515           0.481           0.553   {txt}
{txt}{hline 100}
{txt}Standard errors clustered by individual in parentheses. *p$<$.1; **p$<$.05; ***p$<$.01.

{com}. 
.   
. 
. *********************************************************************************************************
. *Table F17: Regression estimates for heterogeneity analyses (full models for bottom panels in Figure E6)*
. *********************************************************************************************************
. 
. quietly {c -(}
{txt}
{com}. 
. esttab hseb*, replace nonumbers nogaps noomit label noobs nonotes ///
>   order(_cons) coeflabels(_cons "Intercept" 722.datewave "March 2020" 725.datewave "June 2020" 730.datewave "November 2020" 734.datewave "March 2021" 740.datewave "September 2021" 754.datewave "November 2022" 768.datewave "January 2024") ///
>   b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
>   collabels("") scalars("ife Individual FE" "n N. of observations" "unique N. of unique respondents" "r2 R$^2$") ///
>   mgroups("Economic risk (subj)" "Health risk (pred)" "Economic risk (pred)", pattern(1 0 1 1) span) ///
>   mlabels("Low" "High" "" "") ///
>   addnotes("Standard errors clustered by individual in parentheses. *p$<$.1; **p$<$.05; ***p$<$.01.")
{res}
{txt}{hline 84}
{txt}                     Economic risk (subj)            Health risk (~) Economic risk~)
{txt}                              Low            High                                   
{txt}                                                                                    
{txt}{hline 84}
{txt}Intercept           {res}        7.165***        7.279***        7.469***        7.191***{txt}
                    {res} {ralign 12:{txt:(}0.186{txt:)}}    {ralign 12:{txt:(}0.082{txt:)}}    {ralign 12:{txt:(}0.536{txt:)}}    {ralign 12:{txt:(}0.782{txt:)}}   {txt}
{txt}threatcovid=0       {res}        0.000           0.000           0.000           0.000   {txt}
                    {res} {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}   {txt}
{txt}threatcovid=1       {res}       -0.009           0.591***        1.173           1.074   {txt}
                    {res} {ralign 12:{txt:(}0.375{txt:)}}    {ralign 12:{txt:(}0.151{txt:)}}    {ralign 12:{txt:(}0.764{txt:)}}    {ralign 12:{txt:(}1.143{txt:)}}   {txt}
{txt}March 2020          {res}        0.000           0.000           0.000           0.000   {txt}
                    {res} {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}   {txt}
{txt}June 2020           {res}       -0.758***       -0.560***        0.113          -0.044   {txt}
                    {res} {ralign 12:{txt:(}0.229{txt:)}}    {ralign 12:{txt:(}0.121{txt:)}}    {ralign 12:{txt:(}0.556{txt:)}}    {ralign 12:{txt:(}0.999{txt:)}}   {txt}
{txt}November 2020       {res}       -0.116          -0.540***       -0.442          -2.941***{txt}
                    {res} {ralign 12:{txt:(}0.229{txt:)}}    {ralign 12:{txt:(}0.125{txt:)}}    {ralign 12:{txt:(}0.592{txt:)}}    {ralign 12:{txt:(}1.048{txt:)}}   {txt}
{txt}March 2021          {res}       -1.086***       -0.958***       -0.200          -1.391   {txt}
                    {res} {ralign 12:{txt:(}0.235{txt:)}}    {ralign 12:{txt:(}0.136{txt:)}}    {ralign 12:{txt:(}0.574{txt:)}}    {ralign 12:{txt:(}1.038{txt:)}}   {txt}
{txt}September 2021      {res}       -0.871***       -0.810***       -0.237           1.674   {txt}
                    {res} {ralign 12:{txt:(}0.248{txt:)}}    {ralign 12:{txt:(}0.159{txt:)}}    {ralign 12:{txt:(}0.637{txt:)}}    {ralign 12:{txt:(}1.235{txt:)}}   {txt}
{txt}November 2022       {res}       -0.943***       -0.811***       -1.186*         -0.808   {txt}
                    {res} {ralign 12:{txt:(}0.241{txt:)}}    {ralign 12:{txt:(}0.154{txt:)}}    {ralign 12:{txt:(}0.634{txt:)}}    {ralign 12:{txt:(}1.195{txt:)}}   {txt}
{txt}January 2024        {res}       -0.813***       -0.757***       -0.755          -0.831   {txt}
                    {res} {ralign 12:{txt:(}0.235{txt:)}}    {ralign 12:{txt:(}0.154{txt:)}}    {ralign 12:{txt:(}0.648{txt:)}}    {ralign 12:{txt:(}1.193{txt:)}}   {txt}
{txt}threatcovid=0 # ~722{res}        0.000           0.000           0.000           0.000   {txt}
                    {res} {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}   {txt}
{txt}threatcovid=1 # ~725{res}        1.172**         0.090          -0.670          -1.614   {txt}
                    {res} {ralign 12:{txt:(}0.463{txt:)}}    {ralign 12:{txt:(}0.226{txt:)}}    {ralign 12:{txt:(}0.980{txt:)}}    {ralign 12:{txt:(}1.867{txt:)}}   {txt}
{txt}threatcovid=1 # ~730{res}        0.256          -0.031          -0.962           0.496   {txt}
                    {res} {ralign 12:{txt:(}0.454{txt:)}}    {ralign 12:{txt:(}0.216{txt:)}}    {ralign 12:{txt:(}0.989{txt:)}}    {ralign 12:{txt:(}1.786{txt:)}}   {txt}
{txt}threatcovid=1 # ~734{res}        0.635          -0.219          -0.437          -1.142   {txt}
                    {res} {ralign 12:{txt:(}0.444{txt:)}}    {ralign 12:{txt:(}0.246{txt:)}}    {ralign 12:{txt:(}1.023{txt:)}}    {ralign 12:{txt:(}1.824{txt:)}}   {txt}
{txt}threatcovid=1 # ~740{res}        0.079          -0.755***       -1.623          -3.780*  {txt}
                    {res} {ralign 12:{txt:(}0.485{txt:)}}    {ralign 12:{txt:(}0.277{txt:)}}    {ralign 12:{txt:(}1.212{txt:)}}    {ralign 12:{txt:(}1.969{txt:)}}   {txt}
{txt}threatcovid=1 # ~754{res}       -0.405          -1.345***       -1.071          -0.324   {txt}
                    {res} {ralign 12:{txt:(}0.438{txt:)}}    {ralign 12:{txt:(}0.254{txt:)}}    {ralign 12:{txt:(}1.037{txt:)}}    {ralign 12:{txt:(}1.861{txt:)}}   {txt}
{txt}threatcovid=1 # ~768{res}       -1.036**        -1.529***       -2.340**        -3.194   {txt}
                    {res} {ralign 12:{txt:(}0.438{txt:)}}    {ralign 12:{txt:(}0.271{txt:)}}    {ralign 12:{txt:(}1.123{txt:)}}    {ralign 12:{txt:(}1.984{txt:)}}   {txt}
{txt}Fitted values       {res}                                       -0.060                   {txt}
                    {res}                                 {ralign 12:{txt:(}0.121{txt:)}}                   {txt}
{txt}threatcovid=1 # Fi~s{res}                                       -0.120                   {txt}
                    {res}                                 {ralign 12:{txt:(}0.173{txt:)}}                   {txt}
{txt}datewave=725 # Fit~s{res}                                       -0.118                   {txt}
                    {res}                                 {ralign 12:{txt:(}0.124{txt:)}}                   {txt}
{txt}datewave=730 # Fit~s{res}                                        0.006                   {txt}
                    {res}                                 {ralign 12:{txt:(}0.131{txt:)}}                   {txt}
{txt}datewave=734 # Fit~s{res}                                       -0.162                   {txt}
                    {res}                                 {ralign 12:{txt:(}0.127{txt:)}}                   {txt}
{txt}datewave=740 # Fit~s{res}                                       -0.115                   {txt}
                    {res}                                 {ralign 12:{txt:(}0.141{txt:)}}                   {txt}
{txt}datewave=754 # Fit~s{res}                                        0.086                   {txt}
                    {res}                                 {ralign 12:{txt:(}0.138{txt:)}}                   {txt}
{txt}datewave=768 # Fit~s{res}                                        0.009                   {txt}
                    {res}                                 {ralign 12:{txt:(}0.142{txt:)}}                   {txt}
{txt}threatcovid=1 # da~F{res}                                        0.169                   {txt}
                    {res}                                 {ralign 12:{txt:(}0.214{txt:)}}                   {txt}
{txt}threatcovid=1 # da~F{res}                                        0.191                   {txt}
                    {res}                                 {ralign 12:{txt:(}0.222{txt:)}}                   {txt}
{txt}threatcovid=1 # da~F{res}                                        0.059                   {txt}
                    {res}                                 {ralign 12:{txt:(}0.228{txt:)}}                   {txt}
{txt}threatcovid=1 # da~F{res}                                        0.196                   {txt}
                    {res}                                 {ralign 12:{txt:(}0.275{txt:)}}                   {txt}
{txt}threatcovid=1 # da~F{res}                                       -0.034                   {txt}
                    {res}                                 {ralign 12:{txt:(}0.232{txt:)}}                   {txt}
{txt}threatcovid=1 # da~F{res}                                        0.201                   {txt}
                    {res}                                 {ralign 12:{txt:(}0.247{txt:)}}                   {txt}
{txt}Fitted values       {res}                                                        0.007   {txt}
                    {res}                                                 {ralign 12:{txt:(}0.371{txt:)}}   {txt}
{txt}threatcovid=1 # Fi~s{res}                                                       -0.224   {txt}
                    {res}                                                 {ralign 12:{txt:(}0.542{txt:)}}   {txt}
{txt}datewave=725 # Fit~s{res}                                                       -0.217   {txt}
                    {res}                                                 {ralign 12:{txt:(}0.464{txt:)}}   {txt}
{txt}datewave=730 # Fit~s{res}                                                        1.165** {txt}
                    {res}                                                 {ralign 12:{txt:(}0.487{txt:)}}   {txt}
{txt}datewave=734 # Fit~s{res}                                                        0.195   {txt}
                    {res}                                                 {ralign 12:{txt:(}0.483{txt:)}}   {txt}
{txt}datewave=740 # Fit~s{res}                                                       -1.128** {txt}
                    {res}                                                 {ralign 12:{txt:(}0.571{txt:)}}   {txt}
{txt}datewave=754 # Fit~s{res}                                                       -0.029   {txt}
                    {res}                                                 {ralign 12:{txt:(}0.549{txt:)}}   {txt}
{txt}datewave=768 # Fit~s{res}                                                        0.021   {txt}
                    {res}                                                 {ralign 12:{txt:(}0.546{txt:)}}   {txt}
{txt}threatcovid=1 # da~F{res}                                                        0.806   {txt}
                    {res}                                                 {ralign 12:{txt:(}0.863{txt:)}}   {txt}
{txt}threatcovid=1 # da~F{res}                                                       -0.285   {txt}
                    {res}                                                 {ralign 12:{txt:(}0.829{txt:)}}   {txt}
{txt}threatcovid=1 # da~F{res}                                                        0.459   {txt}
                    {res}                                                 {ralign 12:{txt:(}0.844{txt:)}}   {txt}
{txt}threatcovid=1 # da~F{res}                                                        1.404   {txt}
                    {res}                                                 {ralign 12:{txt:(}0.918{txt:)}}   {txt}
{txt}threatcovid=1 # da~F{res}                                                       -0.391   {txt}
                    {res}                                                 {ralign 12:{txt:(}0.861{txt:)}}   {txt}
{txt}threatcovid=1 # da~F{res}                                                        0.822   {txt}
                    {res}                                                 {ralign 12:{txt:(}0.910{txt:)}}   {txt}
{txt}{hline 84}
{txt}Individual FE       {res}          Yes             Yes             Yes             Yes   {txt}
{txt}N. of observations  {res}         2814            5352            7627            9041   {txt}
{txt}N. of unique respo~s{res}          884            1501            1758            2177   {txt}
{txt}R$^2$               {res}        0.596           0.547           0.524           0.533   {txt}
{txt}{hline 84}
{txt}Standard errors clustered by individual in parentheses. *p$<$.1; **p$<$.05; ***p$<$.01.

{com}. 
.   
.   
. 
. quietly {c -(}
{txt}
{com}. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}E:\Dropbox\acad_afalco\CoronaSUTVA\PSRM Submission\Replication\Replication_log.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}20 May 2025, 12:12:16
{txt}{.-}
{smcl}
{txt}{sf}{ul off}